MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641063686 A) filed by Nandha Engineering College on May 20, 2026, for Hybrid Deep Learning Framework For Early Forest Fire Risk Prediction Using Cnn-Lstm And Environmental Data Fusion.
Inventors include S. Pranesh; Deepu Kumar Ram; and Dr. S. Devi.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention discloses a hybrid deep learning framework for early forest fire risk prediction using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and environmental data fusion techniques. The system integrates multi-modal data sources including satellite imagery, loT-based environmental sensors, and meteorological parameters to analyze spatial and temporal environmental conditions associated with forest fire occurrence. The CNN model extracts spatial features such as vegetation dryness, thermal hotspots, and terrain patterns from satellite images, while the LSTM model analyzes sequential climatic variations including temperature, humidity, rainfall, and wind speed. A data fusion mechanism combines the extracted features to generate accurate fire risk classifications and early warning alerts. The framework further provides real-time visualization dashboards, hotspot mapping, and decision-support mechanisms for forest management authorities. The invention enables proactive forest fire prevention, efficient resource allocation, and sustainable environmental conservation through intelligent prediction and monitoring capabilities.
Disclaimer: Curated by HT Syndication.
