Machine Learning for Wildfire Prediction and Ecosystem Protection
Abstract
Wildfires have become increasingly frequent and destructive due to climate change, necessitating early detection and prevention strategies. This paper examines the application of machine learning in wildfire prediction, including AI-driven satellite imagery analysis, real-time sensor data monitoring, and risk assessment modeling. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are used to detect fire-prone areas and optimize firefighting resource allocation. Case studies demonstrate the effectiveness of AI-driven early warning systems in reducing wildfire damage and preserving biodiversity.
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References
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