High-Resolution Prediction of Forest Fire Incidence using Artificial Neural Networks
DOI:
https://doi.org/10.36808/if/2024/v150i3/170243Keywords:
Artificial Neural Network (ANN), Dry Deciduous, Forest Fire, GIS and Sensitivity Analysis, Climatic Factors.Abstract
Forest fire is the major contributor to reduction in ecosystem services rendered by forests. Intelligent prediction models can predict occurrence and spread of forest fire in vulnerable zones so that it can be controlled and managed. In this study, AI based artificial neural network model predicted fire class in different forests types /zones for Kothagudem district in state of Telangana, India. The climatic factors like solar radiation, temperature, wind gust and wind direction contributed for the ignition and intensification of fire incidence. IDW analysis used in GIS to extract meteorological values for non fire points. For a period of 2018 to 2020, a total 41.68% of fire incidents occurred in dry deciduous forests of the district. Class 2 and Class 3 fires were dominant where 69% of the fires occurred between 1 pm to 2 pm during the month of March & April for 2018-2020. Trained Neural network model with feed forward back propagated algorithm predicted class of fire and no-fire incidence with coefficient of correlation more than 0.98 and fire points at 50m resolution.References
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