Extreme Learning Machine Approach for Prediction of Forest Fires using Topographical and Metrological Data of Vietnam

A problem is a well-posed problem if it satisfy, solution existence, solution uniqueness and non- perturbation conditions. Ill-posed problems or inverse problems are the special case of well-posed problem, because inverse of major function does not exist. Forest fire prediction is an inverse problem. Forest are the largest natural resources. Forest fire is a calamity, it is a threat to the entire regime of flora and fauna. Forest fire mitigation is essential because it can devastate biodiversity, wild life and can cause economic loss. In this research work extreme learning machine is used, because it has capability prediction problem solves with better generalization and fast learning speed. ELM is a new approach to be used for forest fire prediction. Presented work predict the forest fire occurrence with the help of topographical and metrological data, with parameters slope, Aspect, Elevation, NDVI, Distance to road, Distance to residential area, Land use, Temperature, Wind speed, Rainfall and forest fire occurrence. The motivation behind this work is to predict the forest fire to provide better way of management for this tragedy. In this research work a relationship is being established between forest fire causing factors and forest fire occurrence using historical data. The used database is already existing data of 540 historical locations of Vietnam. Experiments are conducted on different data partitions of availed data with different activation functions. On the basis of accuracy of model, sigmoid function found to be best and suggested to be used further for forest fire prediction.