Integration of machine learning algorithm using spatial semi supervised classification in FWI data

Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and biological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and essential for forest fire control. Prediction of the forest fire dangerous area could be helpful to increase the efficiency of forest fire management. The ability to quantify the ignition risk could lead to a more informed allocation of fire prevention resources. This paper puts forward an efficient system to predict the forest fires in the forest fire spatial data using SMO and Parallel Artificial Neural Networks. Finally, since large fires are rare dealings, outlier detection techniques will also be addressed.