Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

• Published in: Earth science informatics Vol. 16; no. 1; pp. 397 - 414
• Main Authors: Akıncı, Hazan Alkan, Akıncı, Halil
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023
• Subjects: Earth and Environmental Science; Earth Sciences; Earth System Sciences; Information Systems Applications (incl.Internet); Ontology; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; GIS; Manavgat; Forest fires; Machine learning; Susceptibility mapping
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-023-00953-5

This study primarily aims to produce forest fire susceptibility maps for the Manavgat district of Antalya province in Turkey using different machine learning (ML) techniques. Forest fire inventory data were obtained from the General Directorate of Forestry. The inventory data comprise a total of 545 forest fire ignition points during the years 2013–2021. For model training and validation, 70% and 30% of these points were used, respectively. Average annual temperature, average annual rainfall, aspect, distance to rivers, elevation, distance to settlements, forest type, distance to roads, land cover, plan curvature, slope, solar radiation, tree cover density, topographic wetness index, and wind effect parameters were used in the study. Multicollinearity analysis of these 15 factors showed that they are independent of each other. Tree-based ML models, namely, eXtreme gradient boosting (XGBoost), random forest, and gradient boosting machine, as well as artificial neural networks (ANN) were used to produce forest fire susceptibility maps. The metrics of overall accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AU-ROC) were used to evaluate the performance of the ML models. Based on our results, the XGBoost model revealed the most appropriate susceptibility map that could be used for fire prevention measures.