ML-Based Wildfire Prediction and Detection

This paper provides a comprehensive study on prediction and detection of wildfire using Machine Learning and Deep Learning algorithms. Due to the current environmental trends, wildfire possess a great threat to the ecosystem and human lives at a great cost. Multiple factors are the root cause for wildfires which include environmental factors like temperature, humidity, air pressure index, forest terranean, vegetation. Taking these factors into consideration, a Machine Learning model was built considering diverse algorithms to learn the previous trends and predict future wildfires instances. Based on the satellite imagery of previous wildfires, using CNN and AlexNet algorithms to detect wildfires that are currently taking place for early detection so to contain and control the fire without it causing any damage. Amalgamating these two algorithms, in a single graphical user interface, enhances user accessibility and convenience, providing an invaluable tool in wildfire management. The algorithm achieved an accuracy of average 96.33 % to predict wildfires and was able to detect them based on images at the rate of 93.66%.