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
Hasan Tonbul
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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.

Abstract Image

整合集合机器学习和可解释人工智能,加强图尔基耶地中海地区的森林火灾易感性分析和风险评估
森林火灾对地中海地区的生态系统构成严重威胁;因此,图尔基耶地中海地区 2021 年的火灾凸显了对准确且可解释的森林火灾易发性(FFS)绘图的需求。本研究针对梅尔辛省、安塔利亚省和穆格拉省的森林火灾易发性绘图提出了一种创新方法,将机器学习(ML)模型与可解释人工智能(XAI)相结合。该方法采用了三种最先进的 ML 模型:极梯度提升 (XGBoost)、梯度提升机 (GBM) 和轻梯度提升机 (LightGBM)。这些模型利用 14 个火灾条件因子生成了火灾分布图,其中包括气象、地形、环境和人为因素。LightGBM 表现出色,获得了最高的准确率(0.897),超过了 GBM(0.881)和 XGBoost(0.851)。McNemar 统计检验表明,XGBoost 与 GBM 和 LightGBM 的预测能力存在显著差异,而 GBM 与 LightGBM 之间则没有显著差异。信息增益分析和 SHapley Additive exPlanations(SHAP)分析用于增强模型的可解释性和验证特征的重要性。两种方法都认为,对 FFS 影响最大的变量是土壤水分、帕尔默干旱严重程度指数(PDSI)和地表温度(LST)。另一方面,SHAP 图显示了这些因素与火灾易感性之间复杂的非线性关系。同时,LST 的大幅上升会增加火灾风险;而土壤水分值和 PDSI 的升高则会降低火灾风险的可能性。这项研究还通过应用 XAI,为识别地中海生态系统中的火灾风险驱动因素建立了一个透明的基础,从而为森林火灾地图的可解释性和实用性概念做出了贡献。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
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.
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