Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran

Golestan National Park is one of the oldest biosphere reserves exposed to environmental hazards due to growing demand, geographical location of the park, mountainous conditions, and developments in the last five decades. This study aimed to evaluate potential environmental hazards using machine-learning techniques. This study applied maximum entropy, random forest, boosted regression tree, generalized additive model, and support vector machine methods to model environmental hazards and evaluated the impact of affecting agents and their area of influence. After data collection and preprocessing, the models were implemented, tuned, and trained, and their accuracies were determined using the “receiver operating characteristic curve”. The results indicate the high importance of climatic and human variables, including rainfall, temperature, presence of shepherds, and villagers for fire hazards, elevation, transit roads, temperature, and rainfall for the formation of floodplains, and elevation, transit roads, rainfall, and topographic wetness index in the occurrence of landslides in the national park. The boosted regression tree model with a “AUC value” of 0.98 for flooding, 0.97 for fire, and 0.93 for landslides hazards, had the best performance. The modeling estimated that, on average, 16.2% of the area of Golestan National Park has a high potential for landslides, 14% has a high potential for fire, and 7.2% has a high potential for flooding. So, results of this study can be applied by land use planners, decision makers, and managers of various organizations to decrease effects of these hazards Golestan National Park (GNP).