Description:Title: Early Forest Fire Prediction Using IoT and AI Technologies
Field of the Invention
[0001] The invention described in the document focuses on developing a proactive forest fire prediction system leveraging cutting-edge IoT (Internet of Things) and AI (Artificial Intelligence) technologies. The system integrates real-time environmental monitoring through smart sensor boxes equipped with temperature, humidity, rainfall, and wind speed sensors. These sensors wirelessly transmit data to a centralized platform, where a machine learning model based on Support Vector Machines (SVM) processes the information to predict potential forest fire outbreaks. Upon identifying threats, the system triggers advanced image confirmation via YOLOv8, ensuring precise detection while minimizing false alarms.
[0002] The novelty of the invention lies in its ability to provide a scalable, cost-effective, and reliable early warning system for forest fires. By incorporating AI-driven analysis and IoT-enabled real-time data collection, it surpasses existing satellite and sensor-based solutions that often suffer from delays, limited data inputs, and high false alarm rates. Additionally, the invention emphasizes sustainability by safeguarding biodiversity, reducing ecological damage, and mitigating climate change impacts, aligning closely with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land). This forward-thinking solution not only enhances forest management practices but also serves as a vital tool.
Background
[0003] Wildfires are a critical global issue, causing extensive environmental, economic, and societal damage. In India, forest fires have resulted in the loss of millions of hectares of tree cover and biodiversity, exacerbating climate change and contributing to ecological degradation. Existing fire detection systems, such as satellite-based monitoring, camera networks, and weather-dependent tools, often suffer from delays, limited scalability, and high false alarm rates. These limitations hinder timely intervention and proactive management, leaving significant gaps in wildfire prevention efforts. As the frequency and intensity of forest fires increase, there is a pressing need for innovative solutions that can provide accurate, real-time predictions and alerts to mitigate the devastating impacts on ecosystems, communities, and the economy.
[0004] Wildfires pose a significant threat to both the environment and society, leading to devastating consequences such as loss of biodiversity, economic damage, and adverse health effects. In India, the escalating frequency and severity of forest fires have emerged as a pressing social issue, necessitating urgent intervention. From 2001 to 2022, India witnessed the loss of approximately 3.59 lakh hectares of tree cover to fires, along with 2.15 million hectares lost due to various factors contributing to forest degradation. The economic toll of these wildfires is staggering, estimated at around 440 crore rupees annually (about $107 million). This financial burden does not even account for the intangible losses, including biodiversity depletion, nutrient loss, and soil degradation, which further exacerbate the ecological impact.
[0005] One significant real-life incident of a forest fire that caused substantial loss occurred in the Amazon rainforest in August 2019. This event gained widespread attention due to the Amazon's vital ecological role as the world's largest rainforest and its significance in combating climate change.
, C , Claims:[1]
A wildfire monitoring system comprising LoRa-enabled end devices that collect environmental data, transmit it through gateways using various communication protocols (3G, 4G, 5G, and Wi-Fi), and provide real-time updates to a central network server for analysis.
[2]
A network server configured to process data received from multiple LoRa gateways and integrate it with a web application, enabling real-time alerts and decision-making for fire departments, users, and the public.
[3]
The use of a scalable and multi-channel communication architecture to ensure seamless data transmission and reliable monitoring of wildfire-prone areas, even in remote or under-connected regions.
[4]
A web application connected to the network server that visualizes wildfire data, generates actionable alerts, and provides access to users, fire departments, and the public for effective wildfire response and management.