Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Abstract

Abstract Forest fires in Türkiye have devastated 2.5 million hectares of habitat over four decades, posing a grave threat to Mediterranean forest ecosystems. This study compares machine learning techniques: Decision Trees (DT), Naive Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), for predicting forest fire vulnerability. Using a dataset encompassing various factors like precipitation, soil moisture, temperature, humidity, wind speed, land cover, elevation, aspect, slope, proximity to roads/electricity networks, and population density, the models were trained and tested. The dataset classified vulnerability into four classes: very low, low, moderate, and high. Evaluation metrics included overall accuracy, precision, sensitivity, F1-score, Cohen kappa, and cross-validation (CV).RF exhibited the highest performance (accuracy: 0.80, precision: 0.78, sensitivity: 0.80, F1-score: 0.78, Cohen kappa: 0.71, average CV: 0.71), predicting fire vulnerability classes very low (14.99%), low (0.68%), moderate (65.41%), and high (18.90%) with notable accuracy. DT yielded consistent results, while NB performed stably, though slightly lower than RF and DT. However, ANN and SVM demonstrated lower performance and higher variability. These findings advocate for RF as the most accurate algorithm for forest fire risk prediction, emphasizing its crucial role in proactive fire risk management strategies.