Parallel SVM model for forest fire prediction

Abstract

Forest fire is considered as one of the main cause of the environmental hazard that provides many negative effects. Effective Forest Fire prediction models help to take the necessary steps to prevent forest fire and its negative effects. Existing methods of Cascade Correlation Network (CCN), Radial Basis Function (RBF) and Support Vector Machine (SVM) were applied for the forest fire prediction. Existing methods have the limitations of over fitting problems and lower efficiency in prediction. Existing methods in forest fire prediction have lower efficiency in large dataset due to overfitting problem in the models. The parallel SVM method is developed in this research for reliable performance of the Forest Fire Prediction. Conventional SVM has a higher efficiency in predicting the small fire and has lower efficiency in predicting large fire. The SPARK and PySpark were applied to perform the data segmentation and feature selection in the prediction process. A parallel SVM model is developed to train the meteorological data and predict the forest fire effectively. The parallel SVM model reduces the computational time and high storage required for the analysis. Parallel SVM considers the Forecast Weather Index (FWI) and some weather parameters for the prediction of a forest fire. The parallel SVM model is evaluated on the Indian and Portugal data to analyze the efficiency of the model. The parallel SVM model has the 63.45 RMSE and SVM method has 63.5 RMSE in the Portugal data.