Hybrid models based on deep learning neural network and optimization algorithms for the spatial prediction of tropical forest fire susceptibility in Nghe An province, Vietnam

Abstract

The main objective of this study was to produce forest fire susceptibility maps in the Nghe An province of Vietnam using machine learning models and GIS, namely Deep Neural Network (DNN), Hunger Games Search (HGS), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Adaboost (ADB), and Support Vector Machine (SVM). The application of these models included 1042 current and former forest fire points and 14 conditioning factors. The dataset was divided with a ratio of 70/30, with 70% for building the model and the remaining 30% for testing it. Each model was evaluated by various statistical indices and the results show that HGS performed best in constructing susceptibility maps and improving the performance of the DNN model compared to the reference models, with the areas under the receiver operating characteristic curves (AUROC) of 0.967 The findings of this research may support decision-makers on sustainable land-use planning.