Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region
IF 2.7 4区 地球科学Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0
Abstract
Forest fires pose a serious risk to ecosystems in the Mediterranean region; thus, 2021 fires in the Mediterranean region of Türkiye emphasize the requirement for accurate and interpretable forest fire susceptibility (FFS) mapping. This study presents an innovative approach to FFS mapping for the Mersin, Antalya, and Mugla provinces, integrating machine learning (ML) models with Explainable Artificial Intelligence (XAI). The methodology employs three state-of-the-art ML models: eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Light Gradient-Boosting Machine (LightGBM). These models generated FFS maps using 14 fire conditioning factors, including meteorological, topographic, environmental, and anthropogenic factors. LightGBM demonstrated outstanding performance, acquiring the highest accuracy (0.897), outperforming GBM (0.881) and XGBoost (0.851). McNemar’s statistical test demonstrated significant differences in the predictive capabilities between XGBoost and both GBM and LightGBM, whereas no significant difference was found between GBM and LightGBM. Information Gain and SHapley Additive exPlanations (SHAP) analyses were applied to enhance model interpretability and validate feature importance. Both methods agreed that the most influential variables in FFS are soil moisture, Palmer Drought Severity Index (PDSI), and Land Surface Temperature (LST). On the other hand, SHAP plots revealed complex, nonlinear relationships between these factors and fire susceptibility. At the same time, a high increase in LST enhances the risk of fires; higher soil moisture values and the PDSI decrease the possibility of fire risk. This research also contributes to the concept of FFS mapping interpretability and operational utility with the application of XAI, which establishes a transparent basis for identifying fire risk drivers in Mediterranean ecosystems.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.