Intelligent Wildfire Prediction and Early Warning System Using Deep Learning and Remote Sensing Data
Abstract
Climate change and rising global temperatures have increased the frequency and severity of wildfires worldwide, necessitating intelligent early warning systems. This paper presents a deep learning-based wildfire prediction framework that integrates satellite imagery, meteorological parameters, vegetation indices, and environmental sensor data. The proposed model combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for temporal forecasting. Geographic Information System (GIS) data are incorporated to improve spatial prediction accuracy. Experimental evaluation demonstrates superior wildfire prediction performance compared with traditional statistical models, enabling earlier risk identification and improved disaster preparedness. The proposed framework contributes to environmental protection by supporting proactive wildfire management and reducing ecological and economic losses.
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