1. A smart rainwater prediction and redistribution grid comprising: a plurality of IoT sensors configured to measure soil moisture, temperature, humidity, and rainfall; a machine learning prediction module configured to forecast short-term soil moisture levels and rainfall using regression and deep learning models; a microcontroller-based control unit connected to pumps and solenoid valves, configured to redistribute stored rainwater from surplus regions to deficit regions; a central rainwater storage tank equipped with water level sensors; and an IoT dashboard configured to visualize live soil parameters, prediction results, and redistribution actions in real time.

2. A method for smart rainwater prediction and redistribution, comprising: collecting soil moisture, temperature, humidity, and rainfall data using IoT sensors; preprocessing the collected data and inputting it into a machine learning prediction model; generating short-term forecasts of soil moisture and rainfall; automatically activating pumps and solenoid valves through a microcontroller-based control unit to redistribute water from surplus regions to deficit regions; and monitoring and controlling redistribution actions through an IoT dashboard.

3. The grid as claimed in Claim 1, wherein the machine learning prediction module employs regression and deep learning algorithms to generate soil moisture forecasts.

4. The grid as claimed in Claim 1, wherein the microcontroller-based control unit is configured to deactivate pumps during rainfall events to prevent unnecessary irrigation.

5. The grid as claimed in Claim 1, wherein the IoT dashboard provides real-time notifications, historical data, and manual override options for pump control.

6. The method as claimed in Claim 2, wherein rainfall prediction is combined with soil moisture forecasting to optimize irrigation scheduling and reduce water wastage.

7. The method as claimed in Claim 2, wherein redistribution is carried out using lift irrigation to supply water to higher elevation fields.

8. The grid as claimed in Claim 1, wherein water level sensors are placed both in the central rainwater tank and in the irrigation field to continuously monitor water availability.

9. The grid as claimed in Claim 1, wherein the IoT sensors transmit data to a cloud platform for centralized monitoring and predictive analytics.

10. The method as claimed in Claim 2, wherein redistribution decisions are based on predefined thresholds of soil moisture and tank water levels to ensure efficient water usage.

Description:FIELD OF THE INVENTION
This invention relates to a smart rainwater prediction and redistribution grid.
BACKGROUND OF THE INVENTION
Existing rainwater harvesting setups only store rainwater locally - there is no mechanism for redistributing water from water surplus areas to water deficit areas. Additionally, farmers and communities do not have access to information-based knowledge such as soil moisture levels and advances in rains, and best water supply times resulting in inconsistent crop growing and wastage of resources.
In order to fill such holes; the need for the existence of such a smart and automatic system exists which can track the level of moisture in soil and predict the rainfall with the help of machine learning and then smartly transfer the 'abled' water to the 'needy' regions using the 'IoT Controlled redistribution grid'. Such a system should ensure efficient use of the water, minimize wasting, help agriculture in dry spells of the year and facilitate sustainable management of water across the regions.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
This proposed system introduces Smart Rainwater management and redistribution system based on soil moisture sensing, IoT connection and machine learning (ML) based prediction models. The proposed system is based on precision forecast of the soil moisture with the help of regression and deep learning methods integrated with the real time environmental parameters collected using IoT sensors. First, the soil moisture, temperature and humidity values are collected and processed in order to ensure its input in the prediction model. Second, ML algorithms generate short-time predictions of the soil moisture content, which are used to make the decision on when and how much redistribution of water is required from one area to another. A microcontroller based control unit then carries out automated tasks of irrigation and redistribution with the help of pumps and solenoid valves. For purposes of long term decision making, combined rainfall prediction information is needed in order to reduce unnecessary consumption and better plan irrigation activity. Finally, the system uses IoT dashboard for visualizing specifically live soil parameters, the prediction results or the redistribution actions to be taken for continuous monitoring. The system provides improved water efficiency, reduction of wastage and consistent irrigation support under different climatic conditions.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The present invention discloses a smart rainwater prediction and redistribution grid designed to optimize water usage in agricultural and community settings. The system integrates IoT sensors for monitoring soil moisture, temperature, humidity, and rainfall, thereby providing real-time environmental data. This data is processed by a machine learning prediction module employing regression and deep learning algorithms to forecast short-term soil moisture levels and rainfall patterns. Based on these predictions, the system determines when and how much water redistribution is required between surplus and deficit regions.
A microcontroller-based control unit forms the operational core of the grid, connected to pumps and solenoid valves that automatically execute irrigation and redistribution tasks. The control unit is programmed to deactivate pumps during rainfall events, ensuring efficient water use and preventing unnecessary irrigation. A central rainwater storage tank, equipped with water level sensors, serves as the primary reservoir, while additional sensors placed in irrigation fields continuously monitor water availability.
The system is further enhanced by an IoT dashboard, which provides visualization of live soil parameters, prediction results, and redistribution actions. The dashboard also enables farmers to receive real-time notifications, access historical data, and manually override pump operations if required. Data collected from sensors is transmitted to a cloud platform, allowing centralized monitoring and predictive analytics for long-term water management strategies.
The invention introduces a novel redistribution mechanism that automatically transfers water from surplus regions to deficit regions, including uphill fields through lift irrigation. Redistribution decisions are based on predefined thresholds of soil moisture and tank water levels, ensuring that water is supplied only when necessary. By combining rainfall prediction with soil moisture forecasting, the system minimizes wastage, improves irrigation consistency, and supports sustainable water management under varying climatic conditions.
This smart rainwater prediction and redistribution grid thus represents a significant technical advancement by integrating IoT sensing, machine learning prediction, automated control, and cloud-based monitoring into a unified framework. It provides improved water efficiency, reliable irrigation support, and sustainable resource utilization across diverse agricultural landscapes.
This proposed system introduces Smart Rainwater management and redistribution system based on soil moisture sensing, IoT connection and machine learning (ML) based prediction models. The proposed system is based on precision forecast of the soil moisture with the help of regression and deep learning methods integrated with the real time environmental parameters collected using IoT sensors. First, the soil moisture, temperature and humidity values are collected and processed in order to ensure its input in the prediction model. Second, ML algorithms generate short-time predictions of the soil moisture content, which are used to make the decision on when and how much redistribution of water is required from one area to another. A microcontroller-based control unit then carries out automated tasks of irrigation and redistribution with the help of pumps and solenoid valves. For purposes of long-term decision making, combined rainfall prediction information is needed in order to reduce unnecessary consumption and better plan irrigation activity. Finally, the system uses IoT dashboard for visualizing specifically live soil parameters, the prediction results or the redistribution actions to be taken for continuous monitoring. The system provides improved water efficiency, reduction of wastage and consistent irrigation support under different climatic conditions.
Smart IoT-based lift irrigation is an advanced agricultural system designed to pump water from a central storage source to higher-level fields where natural water flow is not available. In this design, a central main water storage tank collects and stores rainwater using a rain sensing mechanism. When rainfall is detected, rainwater is automatically diverted into the storage tank, increasing the available water supply for irrigation. Two water level sensors are used: one placed inside the central rainwater tank and the other at the irrigation field. These sensors continuously measure water availability and send the data to a NodeMCU controller. All readings, including tank water level, field water status, pump condition, and rain alerts, are uploaded in real time to Firebase Cloud. Farmers can monitor and control the system through a mobile application, receiving instant notifications, historical data, and manual override options for pump control. This IoT-enabled lift irrigation system ensures efficient water usage, reliable supply to higher elevation fields, and effective utilization of rainwater harvesting.
When the field water level becomes low and the central tank has enough stored water, the system automatically activates the lift irrigation pump to transfer water uphill to the fields. If rainfall occurs during irrigation, the pump is immediately turned off to avoid unnecessary water usage and allow natural rainwater to irrigate the crops.
, Claims:1. A smart rainwater prediction and redistribution grid comprising:
a plurality of IoT sensors configured to measure soil moisture, temperature, humidity, and rainfall;
a machine learning prediction module configured to forecast short-term soil moisture levels and rainfall using regression and deep learning models;
a microcontroller-based control unit connected to pumps and solenoid valves, configured to redistribute stored rainwater from surplus regions to deficit regions;
a central rainwater storage tank equipped with water level sensors; and
an IoT dashboard configured to visualize live soil parameters, prediction results, and redistribution actions in real time.
2. A method for smart rainwater prediction and redistribution, comprising:
collecting soil moisture, temperature, humidity, and rainfall data using IoT sensors;
preprocessing the collected data and inputting it into a machine learning prediction model;
generating short-term forecasts of soil moisture and rainfall;
automatically activating pumps and solenoid valves through a microcontroller-based control unit to redistribute water from surplus regions to deficit regions; and
monitoring and controlling redistribution actions through an IoT dashboard.
3. The grid as claimed in Claim 1, wherein the machine learning prediction module employs regression and deep learning algorithms to generate soil moisture forecasts.
4. The grid as claimed in Claim 1, wherein the microcontroller-based control unit is configured to deactivate pumps during rainfall events to prevent unnecessary irrigation.
5. The grid as claimed in Claim 1, wherein the IoT dashboard provides real-time notifications, historical data, and manual override options for pump control.
6. The method as claimed in Claim 2, wherein rainfall prediction is combined with soil moisture forecasting to optimize irrigation scheduling and reduce water wastage.
7. The method as claimed in Claim 2, wherein redistribution is carried out using lift irrigation to supply water to higher elevation fields.
8. The grid as claimed in Claim 1, wherein water level sensors are placed both in the central rainwater tank and in the irrigation field to continuously monitor water availability.
9. The grid as claimed in Claim 1, wherein the IoT sensors transmit data to a cloud platform for centralized monitoring and predictive analytics.
10. The method as claimed in Claim 2, wherein redistribution decisions are based on predefined thresholds of soil moisture and tank water levels to ensure efficient water usage.