An Amalgamation of Decision Tree and Cosine Kernel SYM for the Fire Detection in Forest Cover

Abstract

Forest fire is the crucial tragedy of the ecological system that ruins the features of ecology as well as the economy, whereas putting human lives on risk and sources the lack of biodiversity. The critical factor, to handle comparable events, is Fast detection. Another replacement was made to accomplish this task by employing local sensor-based automatic tools like the sensors presented in the meteorological stations. Meteorological factors (like rain, relative humidity, and temperature) are familiar for impacting the forest fires and various fire indexes, for instance, the Forest Fire Weather Index (FWI) that use corresponding data. It has highlighted the requirement for establishing an approach of adaptive management through current experience moreover, particular factors suggested sector-wise along with short and long term visions. This study delved into a strategy of Data Mining (DM) by integrating the Decision Tree and Cosine kernel SVM (DT-CKSVM) is proposed for forecasting the location burned by a forest fire. The testing of five individual systems of DM (include, Support Vector Machines (SVM) and Random Forests), besides four individual strategies of feature selection (spatial, temporal, components of FWI, and weather attributes) has processed, on prevailing real-world data gathered from India. Four factors of meteorology (temperature, relative humidity, rain and wind) and DT-CKSVM employed by an optimum framework, and it has potential to prophesying the region burned by small fires that are more recurrent. While analyzing the fire events through DM, it has revealed that the DM's interrelationship of trend and its interaction with environmental/meteorological factors, which brings more accurate understanding on the occurrences of the forest fire, hence simplifies in alleviation, manage, avoidance in terms of preserving our valuable forest as well as the ecosystem.