The present subject matter relates to depth analysis of inland waterbodies and,
particularly, to monitoring a variation in depth of the inland waterbodies.
5 BACKGROUND
[0002] Inland waterbodies are aquatic-influenced environments, usually, located within
land boundaries and can include such waterbodies located in coastal areas, adjacent to marine
environment while being land-locked. Inland waterbodies can include lakes, rivers, ponds,
streams, groundwater, springs, cave waters, floodplains, as well as bogs, marshes and swamps
10 and can be either fresh, or saline or a mix of the two, referred to as brackish.
[0003] In general, inland waterbodies may support various kinds of life-forms and,
therefore, require monitoring as to their state in supporting existence of all such life-forms.
Regular monitoring and mapping of surface water is of paramount importance for urban
planning, environment protection, biodiversity protection, and climatic studies. These inland
15 waterbodies are dynamic and tend to shrink and expand with the course of time depending on
the environmental conditions. Thus, analysis of the depth of inland waterbodies for their
detection is, generally, carried out.
BRIEF DESCRIPTION OF DRAWINGS
20 [0004] Figure 1 shows an example of a network environment that can perform depth
analysis of inland waterbodies in accordance with the various techniques of the present subject
matter.
[0005] Figure 2 shows an example of a system for depth analysis of inland waterbodies
in accordance with the various techniques of the present subject matter.
25 [0006] Figure 3 shows an example of a method for depth analysis of inland waterbodies
in accordance with the various techniques of the present subject matter.
DETAILED OVERVIEW
[0007] Inland waterbody depth analysis has traditionally relied on deployment of Internet
30 of Things (IoT) sensors and devices. These sensors are installed in or around waterbodies and
can measure various parameters directly related to water depth. Physical installation of sensors
such as ultrasonic depth gauges, pressure transducers, or radar sensors at strategic locations is
done in or around the waterbody. The IoT sensors collect data at predefined intervals. This data
typically includes water level, temperature, and sometimes additional parameters such as
3
turbidity or conductivity. The collected data is transmitted to a central system via wireless
networks. This can be done in real-time or at scheduled intervals. The collected data is analyzed
to determine the depth of the waterbody. This may involve correlating sensor readings with
known baseline measurements or applying models that account for environmental factors.
5 [0008] Another common technique involves the use of imagery captured by satellites
equipped with various sensors that can detect the presence of water on the Earth's surface. In
one conventional technique, satellite imagery is acquired by satellites pass over regions and
capture images at regular intervals. These images are then transmitted to Earth stations for
processing. The raw satellite data is processed to enhance features and remove noise. This may
10 include correcting for atmospheric distortions, aligning images to a coordinate grid, and
calibrating color bands to reflect true environmental colors. Complex algorithms are applied to
the processed images to identify waterbodies and involve techniques where pixels are classified
as water or not water based on their spectral characteristics.
[0009] While these techniques have been used for monitoring and analyzing the depth of
15 inland waterbodies, they come with limitations. For instance, satellite data can be affected by
cloud cover, and the resolution may not be sufficient to detect small waterbodies, or temporary
waterbodies could be classified as permanent waterbodies. IoT sensors require physical
installation, which can be challenging in remote or inaccessible areas, and they can be prone to
failure or require regular maintenance. They are also very costly to install and maintain, and
20 require highly skilled man-power to work in remote location for operations to run smoothly.
[0010] Existing techniques that rely on singular data sources often result in a narrow and
fragmented representation of inland waterbodies. This can lead to the merging of distinct
adjoining waterbodies into a single entity, which is not accurate. In addition, there may be a
general difficulty in detecting small but ecologically and hydrologically relevant inland water
25 zones, which may be overlooked by current technologies due to their limited spatial resolution
or sensitivity. Existing technologies may struggle to differentiate between short-lived water
patches and consistent waterbodies, potentially classifying ephemeral water as permanent. At
the same time, the operation of current methods can be resource-heavy and cost-intensive,
requiring a substantial investment in both technology and trained manpower for data collection
30 and analysis. Further, the physical infrastructure and complexity of existing methods make
them difficult and expensive to scale up, especially across diverse geographical regions with
varying environmental conditions. There may also be a gap in acquiring timely and precise data
on the conditions of waterbodies, which is exacerbated by the slow processing of data and the
potential for manual errors in data collection and interpretation. Particularly for the IoT sensors35 based techniques, ongoing calibration and maintenance requirements for in-situ sensors can
affect the long-term reliability and accuracy of the data collected.
4
[0011] Some conventional techniques even attempt to integrate data from multiple
sources to improve the accuracy of depth analysis. This can include combining satellite imagery
with sensor data to create a more comprehensive view of waterbody conditions. In this
technique, data from various sources, such as satellites and ground sensors, are combined. This
5 may involve aligning data in time and space to ensure that they are comparable. Models are
developed to interpret the fused data. These models can account for the different types of data
and their respective uncertainties. The integrated data is analyzed to provide a more accurate
assessment of waterbody depth. However, such techniques may face a challenge in integrating
heterogeneous data sources, such as satellite imagery and sensor data, which may have different
10 formats, resolutions, and temporal frequencies. Further, the frequency at which data is collected
and processed, and the potential latency in data availability, can impact the ability to monitor
rapid changes in waterbody depth. At the same time, development of robust algorithms capable
of accurately processing and analyzing the integrated data from various sources, especially in
the face of dynamic environmental conditions, can pose a potential challenge.
15 [0012] The limitations of the existing methods are summarized below:
• Frequent merging of distinct adjoining waterbodies and incorrectly detecting them as
single entities.
• Inability to detect small but vital inland water zones.
• Detection of short-lived water patches as consistent waterbodies due to inability to
20 differentiate between the two.
• Being resource-heavy and cost-heavy in operation.
• Requiring highly trained, on-ground, man power and resources.
• Being difficult and expensive to scale-up.
[0013] The proposed technique of the present subject matter entails a sophisticated
25 analytical technique that leverages multi-source satellite imagery and environmental and
topology data to conduct a high-precision depth analysis of inland waterbodies. The technique
employs advanced computational techniques to integrate and process spatial-temporal data,
adjusting for regional climatic and geographical parameters, to predict and verify the depth
states of waterbodies over a defined temporal cycle. According to an aspect, the technique is
30 predicated on the synthesis of dynamic evaporation and rainfall rates with continuous satellitederived imagery, enabling the calculation of net depth alterations of inland waterbodies. This
is achieved, for example, through the application of differential analysis techniques that factor
in localized evaporative dynamics and precipitation inputs, thus refining the accuracy of depth
estimations for the inland waterbodies. The technique further involves establishing a depth
35 threshold specific to each region, beyond which the depth alterations are processed and
analyzed against multi-temporal satellite data sequences. Using this benchmark, the technique
5
can delineate and classify inland waterbodies that maintain a minimum depth, indicative of
their potential to support aquatic life and other uses over time.
[0014] In essence, the solution provides for the precise identification and monitoring of
inland waterbodies, facilitating the discernment between ephemeral and perennial water
5 features. This is accomplished through a combination of real-time data acquisition,
environmental parameter integration, and the corroboration of predicted waterbody states
against observed states derived from satellite imagery, culminating in a true state assessment
of waterbody depth. The comprehensive technique of the present subject matter is designed to
address the multifaceted challenges of inland waterbody depth analysis. The technique proposes
10 integration of advanced data analytics, remote sensing technology, and environmental science
to provide an accurate, scalable, and cost-effective manner of monitoring and analyzing the
depth of inland waterbodies.
[0015] The technique utilizes a diverse array of satellite imagery sources to capture highresolution, multi-temporal data of inland waterbodies. This imagery is continuously acquired
15 and updated, ensuring that the dataset is dynamic and capable of capturing minor to major
transitions in waterbody states. The integration of multi-source data mitigates the limitations of
single-source data reliance, enhancing the spatial and temporal resolution of the analysis. The
technique involves use of a comprehensive set of environmental parameters that influence
waterbody depth, including climatic factors such as temperature, humidity, wind speed, and
20 solar radiation, as well as geographical parameters including geo-coordinates and topography.
These parameters are used to calculate the evaporation rate (e(t)) and rainfall rate (r(t)), which
are central to determining the net depth variation (dh) of the waterbodies. In an example, various
conventional mathematical, individually or in combination, may be used to determine the the
evaporation rate (e(t)) and rainfall rate (r(t)). The technique utilizes the multi-source imagery
25 to precisely demarcate inland waterbodies by depth, persistence, and regional specificities. This
enables the identification of potential aquaculture sites, the development of water management
tools, and the provision of valuable data for environmental conservation, land-use strategizing,
and climate studies.
[0016] Using the environmental parameters, the technique involves employment of
30 predictive modelling to estimate the predicted net depth variation of the waterbody. This
involves calculating the loss of water due to evaporation and the gain from rainfall over a
predetermined period and using the topographical information of the inland waterbody to
ascertain the net depth variation, which is indicative of the predicted state of the waterbody. A
depth threshold (T) may be established for each region, taking into account the local
35 geographical and climatic nuances. The technique processes the satellite imagery sequence until
the accumulated net depth alteration matches or exceeds the depth threshold. This threshold-
6
based analysis is fine-tuned to ensure that the technique can accurately identify waterbodies
that maintain a depth greater than the minimum threshold, which is indicative of their ability to
support life-forms and other uses.
[0017] Data indicative of the observed state of the waterbody is obtained from secondary
5 sources, such as aerial satellites, at the end of the predetermined period. This observed state is
then used to continually corroborate the predicted state. As an example, the periodicity of
corroboration can be selected based on various factors, such as the period for which the depth
analysis for the inland waterbody is being performed. The corroboration process involves
comparing the predicted depth alterations with the actual satellite imagery to ascertain the true
10 state of the waterbody. At the same time, the technique is designed to continually monitor the
depth of inland waterbodies, updating the predicted state and corroborating it with the observed
state at regular or even irregular intervals. This allows for the incorporation of real-time data,
ensuring that the technique can respond to and account for evolving geographical and climatic
variations.
15 [0018] Therefore, the proposed technique offers a technologically advanced, adaptable,
and reliable method for the depth analysis of inland waterbodies, capable of overcoming the
limitations of existing methods and providing accurate, timely, and actionable data for a wide
range of applications, achieved through the integration of advanced analytical techniques,
multi-source data acquisition, and real-time environmental monitoring. Combining different
20 types of data can lead to a more comprehensive understanding of waterbody dynamics and
potentially reveal correlations that would otherwise be overlooked. At the same time, the
technique has the ability to forecast future waterbody conditions under various environmental
scenarios, aiding in long-term planning and risk assessment.
[0019] By harnessing imagery from a constellation of satellites, the technique achieves a
25 high spatial and temporal resolution in its analysis. This allows for the detection of subtle
changes in waterbody depth that may occur over short periods, providing a more granular and
up-to-date understanding of waterbody dynamics. The technique’s use of differential
evaporation and rainfall time series, adjusted for regional climatic and geographical parameters,
leads to a more accurate calculation of net depth alterations. This precision is further enhanced
30 by corroborating the predicted state with observed satellite imagery, ensuring that the true state
of the waterbody reflects actual conditions. The reliance on satellite imagery and environmental
data reduces the dependency on costly and labor-intensive on-ground sensor networks. This not
just lowers the operational costs but also allows for the system to be scaled to monitor vast and
remote areas without the logistical challenges associated with physical sensor deployments.
35 Automating the depth analysis process minimizes the potential for manual errors and reduces
7
the reliance on extensive manpower and resources. This leads to more efficient resource
utilization and a reduction in the likelihood of human-induced inaccuracies.
[0020] The technique integrates and analyzes real-time data that ensures that the depth
analysis is responsive to immediate environmental changes. This is particularly advantageous
5 for time-sensitive applications, where rapid assessment of waterbody conditions is paramount.
The technique can allow for configurations to flag sudden or unexpected changes in waterbody
depth, which could be indicative of environmental events or anthropogenic impacts.
[0021] The technique of the present subject matter possesses adaptability to diverse
geographical and climatic conditions allows for the application of the system across different
10 regions without the loss of accuracy. This is achieved by calibrating the depth threshold and
environmental parameters to the specificities of each region. Consideration of a wide range of
environmental parameters, including temperature, humidity, wind speed, and solar radiation,
provides a holistic view of the factors influencing waterbody depth. This comprehensive
approach allows for a more nuanced analysis that can account for complex interactions between
15 environmental variables. The technique enables the differentiation between transient water
patches and consistent waterbodies. This is particularly useful for environmental conservation
efforts and urban planning, where the distinction between ephemeral and permanent water
sources is of great relevance.
[0022] The depth analysis technique of the present subject matter provides valuable
20 insights for a range of applications, including aquaculture site identification, water
management, habitat protection, land-use planning, agricultural irrigation planning, and climate
studies. This multidisciplinary utility underscores the system's versatility and its potential to
inform decision-making across various sectors. The technique of the present subject matter can
be used to assess the potential impact of development projects on inland waterbodies and
25 identify long-term data trends. Therefore, the technique can provide insights into the impacts
of climate variability and change on freshwater resources and contribute towards sustainable
practices, such as better allocation of resources for water conservation, extraction, and
distribution efforts, and also serve as a tool for teaching and research, enhancing knowledge in
these fields.
30 [0023] In summary, the present subject matter relates to techniques for depth analysis of
inland waterbodies. The present subject matter is configured to measure the depth alterations
of the inland waterbodies based on various factors, such as spatial evaporation rate based on an
evaporation rate from the inland waterbody owing to at least the climatic parameters associated
with the inland waterbody, and can identify inland waterbodies having depth more than a
35 minimum threshold depth in that region at the end of a predetermined time period. For example,
the end of the predetermined time period may be the beginning of monsoon season after the
8
summer season has ended. The present subject matter involves techniques that measure net
depth alteration of the water in the waterbody using environmental data and applying theoretical
or mathematical relations and then corroborating the results using imagery from multisource
satellite imagery. For instance, the corroboration is conducted when the depth alteration reaches
5 the set depth threshold for the given time period. In this manner, specific inland waterbodies in
the selected region can be selected in which the depth of water is maintained greater than the
minimum depth threshold, for example, indicating the fact that the waterbodies can sustain and
support life-forms over the course of a time period. In said example, the time period can be one
seasonal cycle whose beginning is marked by the day of highest rainfall and the end can be
10 marked by end of summer or beginning of monsoon season. In other words, those waterbodies
can be selected which do not go dry after receiving a full monsoon rainfall and are able to
endure the subcontinental heat season.
DETAILED DESCRIPTION OF THE SUBJECT MATTER
15 [0024] The present subject matter provides a system and a method for depth analysis of
inland waterbodies. The present subject matter overcomes the above-mentioned drawbacks and
achieves unparalleled accuracy of depth analysis and accurate detection of suitable waterbodies
using advanced analytical techniques on available data to gain insights as to the state of the
waterbodies, the state being corroborated, for instance, using multi-source satellite imagery
20 data. With its region-specific adaptability, the technique can detect inland waterbodies across
multiple geographical regions despite their unique environmental (geographical, topographical,
and climatic) parameters.
[0025] As mentioned above, as an example, the technique can commence with obtaining
data of the day with the heaviest rainfall during monsoon season for a given region to ensure
25 that the waterbody sites under analysis are thoroughly filled.
[0026] At the same time, the period for which the depth analysis is to be conducted is also
predefined. The rainfall rate and evaporation rate which varies based on regional factors such
as temperature, humidity, wind speed, and solar radiation are adjusted for each geography based
on local climatic and environmental data. Net depth alterations of the waterbodies are calculated
30 using these values of net evaporation rate and topology of the waterbody. A depth threshold, T,
is established region-wise, taking all geographical parameters into consideration, depending
upon the need of the study and a multisource satellite imagery sequence of inland waterbody in
that region is processed until the accumulated net depth alteration matches at least T. By means
9
of this workflow, the system can give out specific sites in the selected regions with over a
minimum depth threshold T.
[0027] The present subject matter harnesses advanced analytical techniques to improve
the accuracy of depth analysis in a low-cost manner. The present subject matter provides precise
5 identification of waterbodies or sites that do not go dry over the course of the time period of
the depth analysis. Such depth analysis can:
• Identify inland waterbodies that can potentially support aquaculture.
• Can serve as relevant information to the governmental bodies, environmental
conservation agencies, regional planners, urban developers, agricultural
10 stakeholders and climate study researchers.
[0028] The present subject matter disclosed the technique which provides strategic
filtering to discern between transient and permanent inland waterbodies. The present subject
matter provides a method to indirectly measure waterbody depth using principles of evaporation
rate and rainfall rate. The present subject matter enables systematic computation of net depth
15 alterations using localized evaporative dynamics for precise depth analysis. The present subject
matter consistently aggregates imagery from array of aerial satellites so that the imagery dataset
can capture even the minor transitions in waterbodies. The present subject matter is able to
achieve unparalleled depth accuracy and detection precision, even across multiple geographical
regions with its unique geospatial adaptability.
20 [0029] The present subject matter, as described, uses accurate depth analysis for precise
detection of the inland waterbodies, such as pond and lakes. based on multi-source satellite
imaging technology which can be adapted to multiple regions of unique environmental
parameters.
[0030] The approach of the present subject matter harnesses advanced analytical
25 techniques, amalgamated with a continuous inflow of multi-source satellite imagery. By
pooling data from various satellite sources and consistently refreshing this dataset, the method
achieves unparalleled depth accuracy and detection precision, even across multiple
geographical regions with their unique environmental parameters. One example of the
workflow of the technique proposed in the present subject matter, as explained above, is
30 summarized below:
• Region-Specific Initiation: Begin the analytical framework immediately after the day
marking the heaviest rainfall during the monsoon season for each region. This ensures
that potential waterbody sites are thoroughly filled, furnishing an optimal foundation
for the ensuing analysis.
35 • Parameter Calibration and Regional Adjustments:
10
a. Evaporation Rate (e(t)): This represents the volume of water that evaporates
from the waterbody surface over time. The rate varies based on regional factors
such as temperature, humidity, wind speed, and solar radiation.
b. Rainfall Rate (r(t)): This signifies the volume of rainfall contributing to the
5 waterbody. It is determined using historical rainfall data specific to the region and
time of year.
c. Given the diverse geographic locations of the waterbodies, these rates are
adjusted based on local climatic and environmental data.
d. Topology details of the inland waterbody: After calculating the total volume of
10 water displaced due to net evaporation, the details of the topology of the waterbody
will aid in calculating the depth alterations in that waterbody.
• Continuous Multi-Source Satellite Imagery Acquisition:
a. Collect imagery from an array of satellites at intervals specified by dt.
15 b. Consistent updates ensure the dataset remains dynamic, capturing even minor
transitions in waterbodies.
• Evaporative Dynamics Interpretation:
a. Ascertain net depth variation using the following relation, as an example:
dh=(e(t)−r(t)).dt
20 b. Each dh value indicates the depth variation between sequential imagery
acquisitions.
• Depth Threshold Analysis and Fine-Tuning:
a. Establish a depth threshold, T, adjusted per region if needed.
b. Process the imagery sequence until the accumulated dh matches T,
25 incorporating adjustments for regional modifications.
• Precision Waterbody Demarcation Across Diverse Regions:
Upon reaching the stipulated threshold, T, sift through the multi-source imagery
to pinpoint and classify inland waterbodies by depth, persistence, and regional
specificities.
30 [0031] By means of this workflow, the technique is able to identify specific sites in the
selected regions which are over a minimum depth threshold.
[0032] The present technique finds various applications, including but not limited to:
• Identification of potential aquaculture sites for potential inland farming activities
requiring a minimum depth of water over the course of seasons and changes in depth
35 owing to various environmental factors.
• Development of water management tools by precise inland waterbody mapping.
11
• Enabling habitat protection, biodiversity studies, and freshwater resource and
environmental conservation.
• Assistance in land-use strategizing, ensuring optimal space utilization during regional
(urban and/or rural) planning and development.
5 • Determining, from agricultural viewpoint, potential irrigation sources and assistance in
water-intensive crop planning.
• Use in climate studies by offering insights into changing freshwater patterns, potentially
signalling climatic shifts.
10 DESCRIPTION OF DRAWINGS
[0033] Figure 1 illustrates a network environment 100 for implementing the present
subject matter. The network environment 100 includes a system 102 for performing depth
analysis of an inland waterbody which is connected to one or more secondary sources 104A,
104B,….104XYZ (collectively referred to as secondary sources 104 and individually referred
15 to as secondary source 104) over a network 106 to obtain requisite information from the
secondary sources 104. The secondary source 104 can include various data sources including
real-time sources, such as aerial satellites capturing satellite imagery and/or a sensorial system
(having environmental sensors such as temperature, pressure, and humidity sensors) to provide
real-time information, such as satellite imagery and/or environmental information, and/or to the
20 system 102 as well as static sources, such as databases, storing the above information which
can be retrieved by the system 102 and used.
[0034] In one example, the system 102 encompass the hardware and software components
that enable it to perform depth analysis of inland waterbodies. The system is designed to be
robust, scalable, and capable of integrating various data sources to provide accurate and timely
25 information. The system may include or may cooperate with a database management system
for storing, querying, and managing the data collected from various sources, including
environmental parameters and satellite imagery. The system 102 can be designed with a
modular architecture, allowing for easy updates and integration of new sensors, data sources,
or analytical methods and can support scaling up to handle larger geographic areas or increased
30 data volumes without a loss in performance. The system 102 is designed to work with various
data formats and standards, ensuring compatibility with different satellite systems and
environmental sensors and can have an interface for configuring the system 102, initiating
analysis, and visualizing results. In certain cases, the system 102 is designed to automatically
acquire data from secondary sources at specified intervals or upon the occurrence of specific
12
events, such as the end of a predetermined period and incorporates feedback loops to refine
predictive models based on the corroboration of predicted and observed states.
[0035] The secondary sources 104 play a central role in providing the data that the system
102 uses for depth analysis of inland waterbodies. These sources are diverse and can include
5 both hardware and software components designed to collect, store, and transmit environmental
and satellite data. The satellite imagery sources can include, for example, satellite constellations
having a network of earth observation satellites equipped with sensors for capturing highresolution imagery of inland waterbodies, data downlink stations on the ground that receive
data transmissions from the satellites and relay the information to data processing centers or
10 directly to the system 102, imagery processing centers equipped with software for processing
raw satellite data into usable imagery, such as correcting for atmospheric distortions and
aligning images over time. In another example, the satellite imagery sources may be any
combination of all or few of the above satellite imagery sources.
[0036] Further, the environmental data sources can include, for example, sensorial arrays
15 of environmental sensors, such as temperature, humidity, pressure, and wind speed sensors, that
collect real-time data relevant to the waterbody's evaporation and rainfall rates, automated
weather stations that provide comprehensive meteorological data, which can be used to adjust
the evaporation and rainfall rates for the depth analysis. In another example, the environmental
data sources may be any combination of all or few of the above environmental data sources.
20 [0037] The databases of the secondary sources 104 can be collections of data that serve
as repositories for environmental parameters, satellite imagery, topology details, and other
relevant information used by System 102 for depth analysis of inland waterbodies. These
databases are designed to handle large volumes of data, ensure data integrity, and provide quick
access to the stored information. The databases may either be relational databases that can
25 organize data into tables with predefined relationships, which is suitable for structured data
such as sensor readings and satellite metadata. In other case, the databases may be NoSQL
databases usable for unstructured or semi-structured data, such as raw satellite imagery or
complex environmental data, or may be time-series databases designed for storing and
retrieving time-ordered data, such as historical weather data or sensor measurements over time.
30 In another example, the databases may be geospatial databases that are optimized for storing
and querying geospatial data, including satellite imagery and geo-referenced sensor data. In
another example, the databases may include any combination of all or few of the above
databases.
[0038] The sensorial systems of the secondary sources 104 are composed of a variety of
35 sensors and associated infrastructure designed to collect accurate and timely environmental
data. These sensorial systems are characterized by their robustness, power management
13
strategies, and ease of integration with data processing and storage systems. They are built to
operate reliably in diverse environmental conditions for the system 102 to perform depth
analysis of inland waterbodies. The sensorial systems may be networks of physical devices
designed to detect and measure environmental parameters relevant to the depth analysis of
5 inland waterbodies. The sensorial system may include sensors, such as meteorological sensors
that record weather-related data, including temperature, humidity, atmospheric pressure, and
wind speed, evapotranspiration sensor to estimate the rate of evaporation and transpiration from
the waterbody and surrounding land, soil moisture sensors that can measure the moisture
content in the soil, which can influence the water exchange with the waterbody, and
10 hydrological sensors that measure water-related parameters such as water level, flow rate, and
water quality indicators. In another example, the sensors may be any combination of all or few
of the above sensors. The sensorial network architecture may be one of a distributed sensor
network strategically placed in and around the inland waterbody to collect diverse
environmental data, central data loggers that aggregate data from multiple sensors for
15 preprocessing and transmission to the database or directly to the system 102, or a combination
thereof. The secondary sources 104 can use of wireless technologies, such as Wi-Fi, cellular,
and satellite, to transmit data from remote or inaccessible locations to the system 102 or may
use wired connections, say in controlled environments.
[0039] Therefore, the secondary sources 104 are composed of a variety of data collection
20 systems, storage and retrieval systems, and communication infrastructure. They are designed
to provide accurate, timely, and reliable data to the system 102 for the purpose of depth analysis
of inland waterbodies. The design of these secondary sources 104 takes into account the quality
of data, standardization for interoperability, scalability to handle different volumes of data, and
security to protect the data throughout the process.
25 [0040] The network 106 is a communication framework that connects the system 102
with secondary sources 104, facilitating the transfer of data and information for the depth
analysis of inland waterbodies. The network 106 has an architecture that allows for the
integration of various data sources, including sensorial systems and satellite imagery, while
providing the flexibility to adapt to evolving technological and operational demands. The
30 network 106 is designed to be robust, secure, and capable of handling high volumes of data
traffic and may be wired using of fiber-optic cables or Ethernet or wireless using technologies
such as Wi-Fi, LTE, 5G, or satellite communications, or a combination of the two. In an
example, the network 106 can employ a star topology in which, for instance, the secondary
sources 104 are connected to a central hub, which is the system 102, allowing for centralized
35 data collection and processing. In another example, the network 106 can employ a mesh
topology to provide multiple pathways for data transmission, enhancing reliability and
redundancy. Another example may include the network 106 with a combination of both
14
topologies. The network 106 may utilize various communication protocols for data
transmission over the internet, such as the Internet Protocol (IP) utilizing standard protocols,
for instance, Transmission Control Protocol/Internet Protocol (TCP/IP) for reliable data
delivery and User Datagram Protocol (UDP) for streaming data. In another example, the
5 network 106 may utilize custom protocols tailored to the specific data requirements and
constraints of the sensorial systems and satellite data sources.
[0041] In operation, the system 102 can obtain environmental parameters associated with
the inland waterbody that is under consideration for depth analysis and then determine, as part
of the depth analysis, determine the variation in depth of the inland waterbody over a
10 predetermined period. As an example, the beginning of the predefined period for which the
depth analysis is performed is marked by heaviest rainfall in a season in a geographical region
of the inland waterbody. In said example or another, the end of the predefined period can be
defined by a user in terms of no. of days, weeks, months, or hours. In addition, the end of the
predefined period can be automatically determined by the system 102, based on information
15 obtained through the network environment, and is marked by the heaviest rainfall in the
immediately following season in the geographical region of the inland waterbody, thereby,
completely one complete seasonal cycle.
[0042] The environmental parameters can include climatic and geographical parameters
associated with the inland waterbody. In an example, the climatic parameters can include
20 rainfall rate in a geographical region of the inland waterbody, average daily temperatures in the
geographical region of the inland waterbody, and air humidity in the geographical region of the
inland waterbody. In said example or another, the geographical parameters can include a set of
geo-coordinates localizing the location of the inland waterbody and a topography of a
geographical region of the inland waterbody. As explained above, the environmental
25 parameters may either obtained from the sensorial system that either detects or measures, or
both, the environmental parameters or can be obtained from obtained from one or more
databases having stored thereon the environmental parameters.
[0043] Based on the environmental parameters, the system 102 determines a predicted
state of the inland waterbody. The predicted stated of the inland waterbody is the state that the
30 inland waterbody achieves due to loss of water from the inland waterbody over course of the
predetermined period. In addition, the loss of water is directly attributable to the environmental
parameters, such as the temperature in the geographical region of the inland waterbody, the
presence of winds (high or low pressure areas) in or around the geographical region of the
inland waterbody, and the humidity in the vicinity of the inland waterbody, and other such
35 factors that can directly or indirectly affect the evaporation from the inland waterbody or
refilling of water into the inland waterbody.
15
[0044] Once the predicted state is determined, the system 102 is designed to corroborate
the predicted state to ensure that the theoretically determined and predicted state of the inland
waterbody matches with the actual state that the waterbody is in at the end of the predetermined
period of depth analysis. For example, even though theoretically the system 102 can predict
5 that given the environment factors, the waterbody must have a certain depth of water remaining
therein even after the predetermined period, say the inland waterbody experiencing summer
season, but whether in reality the waterbody has any water remaining therein must be verified.
To this end, the system 102 can obtain data indicative of an observed state of the inland
waterbody from the secondary source 104, described above, at an end of the predetermined
10 period. This is based on the fact that neither theoretical prediction nor satellite imagery, if
considered individually, can precisely indicate the state of the inland waterbody. At the end of
the corroboration, the system 102 can ascertain a true state of the inland waterbody which is
indicative of the actual or in-reality state of the inland waterbody at the end of the predetermined
period.
15 [0045] According to one aspect, the system 102 can continually, i.e., periodically for each
predefined period of time that may be successive or not, determine the predicted state of the
inland waterbody and corroborate the predicted state of the inland waterbody against the
observed state. Further, the system 102 can be designed to corroborate the predicted state of the
inland waterbody against the observed state only when a certain condition is met, thereby saving
20 computational resources. In one case, the system 102 can corroborate the predicted state with
the observed state only when a cumulative loss of water from the inland waterbody, at the end
of one or more predetermined periods, is determined to be beyond a threshold loss. As will be
understood, in an example, the cumulative loss of water is the loss of water from the inland
waterbody collectively in the various predetermined periods, within the period for which the
25 depth analysis is being conducted. Further, the threshold loss can be determined and set by an
expert user or can be determined by the system based on the environmental parameters
associated with the inland waterbody, described above.
[0046] Figure 2 illustrates an example of the system 102 that performs depth analysis of
the inland waterbody, in line with the present subject matter. The system 102 can be a
30 computing system ranging from a handheld device to a server computing system and everything
in between. The system 102 can include a processor 202, a memory 204, set of computerexecutable instructions 206 that can be executed for performing the above-elaborated
techniques, and interface(s) 208 by which the system 102 can communicate over the network
106 with the secondary sources 104. The aforementioned operation of the system 102 described
35 with reference to Figure 1 can be performed by the processor 202 by executing the set of
computer-executable instructions 206.
16
[0047] The processor 202 can be a central processing unit (CPU) or a multi-core processor
capable of handling complex computations and data processing tasks. Memory 204 can include
one or both of volatile (RAM) and non-volatile (storage) memory for storing the operating
system, applications, and data. Interfaces 208 can be network interfaces for communication
5 over network 106, including wired or wireless connectivity options. The set of computerexecutable instructions 206 can include the software applications, code, and protocols that
execute the depth analysis techniques, including data acquisition, processing, corroboration,
and reporting.
[0048] In operation, processor 202 obtains the environmental parameters and makes
10 adjustments for climatic and geographic variations in a multi-step process that ensures accurate
depth analysis of inland waterbodies across diverse global regions. The processor 202 can
obtain environmental parameters either from one or more sensorial systems or from one or more
databases that store historical and current readings of environmental parameters, or from a
combination thereof. The sensorial systems may include a network of environmental sensors
15 such as temperature, humidity, wind speed, and solar radiation sensors, which can either
detecting or measure, or both, the environmental parameters, as the case may be, and provide
real-time data regarding the environmental parameters. Databases can provide past climatic
data, which is useful for establishing baseline conditions and trends. In one example, the
databases can be populated with the detected, measured, or otherwise captured readings of the
20 environmental parameters received from the one or more sensorial systens.
[0049] In addition or as an alternative, the processor 202 uses geo-coordinates to localize
each inland waterbody. This step allows the system processor 202 to fetch the correct regional
data and apply the appropriate environmental parameters for that specific location. Additionally
or alternatively, the processor 202 can obtain geographic parameters such as the topography of
25 the region are also obtained, which can influence local climate conditions and waterbody
behaviour, such as water flow patterns and catchment areas.
[0050] The processor 202 may, therefore, be able to make adjustments for climatic and
geographic variations. The processor 202 can obtain and integrate a wide range of
environmental parameters, making regional adjustments, and continuously updating its models
30 to ensure accurate depth analysis of inland waterbodies across the world. The system's
adaptability and scalability make it suitable for a variety of waterbody types and geographic
locations, providing a robust tool for global water resource management.
[0051] For instance, the processor 202 can perform regional calibration and adjusts the
evaporation rate (e(t)) and rainfall rate (r(t)) for each waterbody based on its specific geographic
35 and climatic conditions. This involves calibrating the system to account for regional variations
such as altitude, prevailing wind patterns, and local weather systems. Additionally or
17
alternatively, the processor 202 may make temporal adjustments to account for seasonal
variations and climate anomalies by adjusting the parameters over time. This ensures that the
system remains accurate even in the face of changing weather patterns or events.
[0052] Further, as part of the adjustments, the processor 202 can determine the depth
5 threshold (T) adapted for each region to reflect the local environmental context. In an example,
the threshold is used to determine when a waterbody's depth alteration matches the criteria for
being considered to be at a level that can sustain life or meet other specified criteria. The
processor 202 uses the obtained and adjusted environmental parameters to model the predicted
state of the waterbody. This involves calculating the net depth variation (dh) using, as an
10 example, the relation dh=(e(t)−r(t)).dt, which takes into account the adjusted evaporation and
rainfall rates.
[0053] In addition, the processor 202 can predict the state of an inland waterbody based
on environmental parameters involves a series of computational and analytical steps. The
processor 202 operates by collecting and calibrating environmental parameters, modeling the
15 predicted state of the waterbody based on these parameters, and continuously updating this
model in response to new data. The system's predictive capabilities are designed to account for
the direct impact of environmental factors on water loss, providing a reliable estimation of the
waterbody's state over a predetermined period.
[0054] To begin with, the processor 202 can collect real-time environmental data relevant
20 to the waterbody's location such as climatic data, including temperature, humidity, wind speed,
and solar radiation. Such environmental data can include geographic and topographic data of
the region to provide context for the climatic data.
[0055] As an example, the environmental parameters and other relevant data may be
stored and managed in standardized formats such as GeoJSON (Geo JavaScript Object
25 Notation) format or TopoJSON (Topo JavaScript Object Notation) format, facilitating
interoperability with other techniques and ease of use for various stakeholders. The use of
standardized data formats such as GeoJSON and TopoJSON enhances the interoperability of
the system with other geographic information techniques (GIS) and data platforms. This
facilitates data sharing, collaboration, and integration with existing water management tools
30 and allows for integration for creating a more interconnected and responsive environmental
monitoring framework. The processor 202 may also integrate historical climatic data to
understand typical patterns and anomalies in the waterbody's environment.
[0056] The processor 202 can calibrate the evaporation rate (e(t)) and rainfall rate (r(t))
based on the collected data, adjusting for local climatic variations and geographic specificities
35 and accounts for seasonal changes that affect evaporation and rainfall rates, ensuring that the
model reflects the actual conditions during the predetermined period. The processor 202 can
18
aggregate the depth changes in the inland waterbody over the predetermined period to model
the cumulative effect of environmental factors on the waterbody's depth.
[0057] Further, the processor 202 can perform predictive modelling, for example, using
the equation dh=(e(t)−r(t)).dt, for each interval dt, which represents the change in water depth
5 over, such as one or more successive or cumulative predetermined periods. For instance, based
on the cumulative depth change, the processor 202 can model the predicted state of the
waterbody, which is an estimation of the waterbody's depth at the end of the predetermined
period. In an example, the processor 202 can compare predicted depth against the preestablished depth threshold (T) to determine the waterbody’s ecological integrity or usability.
10 [0058] Further, the processor 202 identifies and acquires data from secondary sources 104
to determine the observed state of the waterbody at the end of a predetermined period. This
observed state data is then used to corroborate the predicted state, ensuring that the depth
analysis so performed is grounded in actual observations.
[0059] For instance, to start with, the processor 202 may identify and/or select appropriate
15 secondary sources 104 that can provide observational data. These sources typically include an
array of aerial satellites equipped with various sensors capable of capturing high-resolution
imagery and other relevant data. Say, at the end of the predetermined period, the processor 202
acquires the latest satellite imagery from the selected secondary sources. This imagery is used
to observe the current state of the waterbody. Additionally or alternatively, the processor 22
20 can collect other relevant data from secondary sources, such as radar, lidar, or infrared sensors,
which can provide additional insights into the waterbody's state.
[0060] In one example in which the processor 202 obtains raw or processed satellite
imagery, the processor 202 can process or trigger processing of the acquired satellite imagery
to extract meaningful information about the waterbody. This may involve techniques such as
25 image segmentation, classification, and change detection. Similar processing may be performed
on the data from other secondary sources 104. Using the processed data, the processor 202
assesses the observed state of the waterbody, for instance, including determining the
waterbody's surface area, perimeter, and other features that can be indicative of its depth.
[0061] Further, the processor 202 can use the observed state for corroborating the
30 predicted state of the inland waterbody to ascertain the true state involves a series of analytical
steps. This process is designed to validate the accuracy of the processor 202 in predicting and
providing a reliable assessment of the waterbody's condition. Here is an elaboration of this
process: For instance, the processor 202 can perform a comparative analysis to identify
discrepancies between the predicted and observed states. This involves comparing parameters
35 such as water surface area, perimeter, and other indicators of water depth and statistical
19
analysis, machine learning models, or other advanced analytical techniques to assess the degree
of correlation between the two states.
[0062] In addition, the processor 202 can analyze the true state data to estimate depth
variation, for instance, by applying temporal and spatial analyses to understand the changes
5 over time and across the waterbody, and integrating this information with environmental
parameters. In an example, the processor 202 may be configured to generate reports and provide
decision support based on the depth variation analysis, offering stakeholders a detailed and
accurate picture of the waterbody's depth dynamics.
[0063] The processor 202 operates for determining the variation in depth of an inland
10 waterbody based on the true state by performing a sequence of analytical steps that translate
the corroborated data into actionable depth measurements. This ensures that the depth variation
reflects the actual conditions of the waterbody. In an example, the processor 202 determines
the depth variation by comparing the estimated depth from the true state data with previous
depth measurements or baseline depth levels established for the waterbody. For instance, the
15 processor 202 can assesses change of depth of the waterbody over the predetermined period by
analyzing the temporal sequence of true state data while, additionally or alternatively, mapping
the spatial variation in depth across different areas of the waterbody, identifying zones of depth
increase or decrease.
[0064] Figure 3 illustrates, as an example, a method 300 for depth analysis of inland
20 waterbodies. In describing examples of Figure 3, reference is made to the examples of Figures.
1 and 2 for purposes of illustrating suitable components or elements for performing a step or
sub-step being described. For the sake of brevity, the detailed operation of the components or
elements has not been repeated herein and will be understood to be associated with the
respective step or sub-step being described. Further, the steps themselves are elaborated in the
25 foregoing description and, except for the broad-level description of the steps, the elucidation of
same is not repeated herein but would be understood per the foregoing description. It can be
appreciated that some of the method steps may be deleted, modified, or more steps may be
added. Also, the steps are not limited by the order in which they are performed. Some of the
steps may be performed simultaneously as well.
30 [0065] Referring to Figure 3, examples of method steps described herein are related to the
system, such as the system 102, to perform depth analysis of the inland waterbody, in line with
the present subject matter. According to one embodiment, the techniques are performed by the
processor 202 executing one or more sequences of software logic instructions that constitute
the set of computer-executable instructions 206 of the system 102. Such instructions may be
35 read into the memory 204 from a machine-readable medium, such as memory storage.
Execution of the sequences of instructions can cause the processor 202 to perform the process
20
steps described herein and in the foregoing description. It is contemplated that, in some
implementations, few or all of the portions of executable instructions may be hosted at a remote
device other than at the system 102. In alternative implementations, at least some hard-wired
circuitry may be used in place of, or in combination with, the software logic instructions to
5 implement examples described herein. Thus, the examples described herein are not limited to
any particular combination of hardware circuitry and software instructions. Further, in other
embodiments, the method 300 may be implemented in its entirety or partially using any other
device than the system 102.
[0066] At block 302, environmental parameters associated with the inland waterbody are
10 obtained, the environmental parameters comprising at least climatic and geographical
parameters associated with the inland waterbody. In this step, systematic computation of net
depth variations in the water level of waterbodies is done through integrated environmental
parameters, such as evaporation rates and rainfall rates.
[0067] At block 304, based on the environmental parameters, a predicted state of the
15 inland waterbody achieved due to loss of water over course of a predetermined period is
determined. The predicted stated of the inland waterbody is the state that the inland waterbody
achieves due to loss of water from the inland waterbody over course of the predetermined
period. In addition, the loss of water is directly attributable to the environmental parameters,
such as the temperature in the geographical region of the inland waterbody, the presence of
20 winds (high or low pressure areas) in or around the geographical region of the inland waterbody,
and the humidity in the vicinity of the inland waterbody, and other such factors that can directly
or indirectly affect the evaporation from the inland waterbody or refilling of water into the
inland waterbody. Therefore, block 304 involves calculating the loss of water due to
evaporation and the gain from rainfall over a predetermined period to ascertain the net depth
25 variation, which is indicative of the predicted state of the waterbody. A depth threshold (T) may
be established for each region, taking into account the local geographical and climatic nuances
and may be used for measuring the net depth variation against the required depth for the inland
waterbody for the waterbody’s usability over the season(s).
[0068] At block 306, data indicative of an observed state of the inland waterbody is
30 obtained from a secondary source at an end of the predetermined period. The observed state of
the inland waterbody can be determined based on satellite imagery, the aerial satellites (that are
source of the satellite imagery) being the secondary source. In this step, aggregation and
consistent updating of multi-source satellite imagery is also performed to map inland
waterbodies over time.
21
[0069] At block 308, the predicted state of the inland waterbody is corroborated based on
the observed state of the inland waterbody, and a true state of the inland waterbody is
ascertained based on the corroboration.
[0070] At block 310, the true variation in depth of the inland waterbody is determined
5 based on the true state of the inland waterbody. Therefore, the waterbody depth (predicted state)
is, first, calculated using evaporation and rainfall dynamics and, then, the predicted state is
corroborated by the observed state (using satellite imagery which shows whether the waterbody
actually has any water or not, even if the predicted state indicates that the waterbody should
have water at the end of the predetermined period or the end of the summer season following a
10 heavy monsoon). This allows for strategic ability to the technique to discern between transient
and permanent inland wate bodies.
[0071] The technique is able to exercise high precision due to diverse satellite data sources
and imagery which are regularly updated. In addition, evolving geographical and climatic
variations are updated in real-time and incorporated as part of the prediction of the present
15 technique. In summary, the present technique achieves high accuracy owing to continuous data
fetching, updating, and processing.
[0072] The concepts and techniques described above are implementations and examples
of the present subject matter. The scope of the subject matter is not limited to only such aspects
as described above. It is contemplated for aspects described herein to extend to individual
20 elements and concepts described herein, independently of other concepts, ideas or system, as
well as for aspects to include combinations of elements recited anywhere in this specification.
Although aspects are described in detail herein with reference to the accompanying drawings,
it is to be understood that the invention is not limited to those precise aspects. As such, many
modifications and variations will be apparent to practitioners skilled in this art. Furthermore, it
25 is contemplated that a particular feature described either individually or as part of an aspect or
example can be combined with other individually described features, or parts of other elements,
even if the other features and aspects make no mention of the particular feature. Thus, the
absence of described combinations should not preclude from claiming rights to such
combinations.
30
Dated: 13th August 2024
Signature
35 Disha Shah - IN/PA-4826
22
I/We claim:
1. A system for performing depth analysis of an inland water body, the system
comprising:
a processor to:
5 obtain environmental parameters associated with the inland water body, the environmental
parameters comprising at least climatic and geographical parameters associated with the inland
water body;
determine, based on the environmental parameters, variation in depth of the inland water body
achieved due to loss of water from the inland water body over course of a predetermined period,
10 wherein the loss of water is directly attributable to the environmental parameters;
obtain data indicative of an observed state of the inland water body from a secondary source at
an end of the predetermined period;
compare the observed state of the inland water body with the variation in depth of inland water
body to ascertain a true state of the inland water body; and
15 determine the atleast depth of the inland waterbody based on the true state of the inland water
body.
2. The system as claimed in claim 1, wherein the environment parameters are obtained
from a sensorial system configured for one of detecting and measuring the environmental
20 parameters.
3. The system as claimed in claim 1, wherein the environmental parameters are obtained
from one or more databases having stored thereon the environmental parameters.
25 4. The system as claimed in claim 1, wherein processor is to determine the loss of water
from the inland water body based on net evaporation rate from the inland water body owing to
at least the environmental parameters associated with the inland water body.
5. The system as claimed in claim 1, wherein the processor is to calculate the loss of water
30 of the inland water body at the end of the predefined period based on the net evaporation rate
from the inland water body and determine the variation in depth of the inland water body based
on the topography of the inland water body.
23
6. The system as claimed in claim 1, wherein the processor is to continually determine
the loss of water from the inland water body and compare the variation in height of the inland
5 water body against the observed state every predefined period of time.
7. The system as claimed in claim 1, wherein the processor is to compare the variation in
height of the inland water body against the observed state when a cumulative loss of water from
the inland water body is determined to be beyond a threshold, the cumulative loss of water
10 being the loss of water from the inland water body at the end of one or more predetermined
periods, the threshold being determined based on the need of the study of the depth analysis
associated with the inland water body.
8. The system as claimed in claim 1, wherein the secondary source of the data indicative
15 of the observed state of the inland water body is an array of aerial satellites, the data comprising
satellite imagery.
9. The system as claimed in claim 1, wherein the climatic parameters comprise rainfall
rate in a geographical region of the inland water body, average daily temperatures in the
20 geographical region of the inland water body, and air humidity in the geographical region of
the inland water body.
10. The system as claimed in claim 1, wherein the geographical parameters comprise a set
of geo-coordinates localizing the location of the inland water body, a topography of a
25 geographical region of the inland water body and topography of the inland waterbody.
11. The system as claimed in claim 1, wherein a beginning of the predefined period is
marked by heaviest rainfall in a season in a geographical region of the inland water body.
30 12. A method for performing depth analysis of an inland water body, the method
comprising:
24
obtaining environmental parameters associated with the inland water body, the environmental
parameters comprising at least climatic and geographical parameters associated with the inland
water body;
determining, based on the topography of the inland water body, a variation in height of the
5 inland water body achieved due to loss of water from the inland water body over course of a
predetermined period, wherein the loss of water is directly attributable to the environmental
parameters;
obtaining data indicative of an observed state of the inland water body from a secondary source
at an end of the predetermined period;
10 comparing the variation in height of the inland water body with the observed state of the inland
water body to ascertain a true state of the inland water body; and
determining the atleast depth of the inland water body based on the true state of the inland water
body.
13. The method as claimed in claim 12, wherein the environment parameters are obtained
15 from a sensorial system configured for one of detecting and measuring the environmental
parameters.
14. The method as claimed in claim 12, wherein the environmental parameters are obtained
from one or more databases having stored thereon the environmental parameters.
20
15. The method as claimed in claim 12, wherein determining the variation in height of the
inland water body comprises ascertaining the loss of water from the inland water body based
on an evaporation rate from the inland water body owing to at least the climatic parameters
associated with the inland water body and the topography of the inland waterbody.
25
16. The method as claimed in claim 12, wherein the determining the variation in height of
the inland water body comprises continually determining the variation in height of the inland
water body and comparing the variation in height of the inland water body against the observed
state every predefined period of time.
30
17. The method as claimed in claim 12, wherein comparing the variation in height of the
inland water body with the observed state is performed when a cumulative variation in height
of inland water body is determined to be beyond a predefined threshold, the cumulative
25
variation in height being a cumulative loss of water from the inland water body at the end of
one or more predetermined periods, the predefined threshold being determined based on the
need of the study of the depth analysis associated with the inland water body.
5 18. The method as claimed in claim 12, wherein the secondary source of the data indicative
of the observed state of the inland water body is an array of aerial satellites, the data comprising
satellite imagery.
19. The method as claimed in claim 12, wherein the climatic parameters comprise rainfall
10 rate in a geographical region of the inland water body, average daily temperatures in the
geographical region of the inland water body, and air humidity in the geographical region of
the inland water body.
20. The method as claimed in claim 12, wherein the geographical parameters comprise a
15 set of geo-coordinates localizing the location of the inland water body, a topography of a
geographical region of the inland water body and a topography of the inland water body.
21. The method as claimed in claim 12, wherein a beginning of the predefined period is
marked by heaviest rainfall in a season in a geographical region of the inland water body.
20
22. The method as claimed in claim 12, wherein determining the variation in the depth of
the inland water body comprises ascertaining the variation in the depth of the inland water body
at the end of the predefined period based on net evaporation rate from the inland water body
and the topography of the inland water body.
25
30
26
ABSTRACT
DEPTH ANALYSIS OF INLAND WATERBODIES
This novel innovation introduces a comprehensive system a