The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence
文献类型：期刊论文
| 作者 | Tosan, Moein2; Nourani, Vahid3,4; Kisi, Ozgur5,6,7; Zhang, Yongqiang8; Kantoush, Sameh A.9; Gebremichael, Mekonnen10; Taghizadeh-Mehrjardi, Ruhollah2,11; Huang, Jinhui Jeanne1,2 |
| 刊名 | CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
 |
| 出版日期 | 4602 |
| 卷号 | N/A |
| 关键词 | Explainable artificial intelligence (XAI) geohazard assessment machine learning SHAP trustworthy AI model interpretability |
| ISSN号 | 1526-1492 |
| DOI | 10.32604/cmes.2025.074768 |
| 产权排序 | 7 |
| 文献子类 | Review ; Early Access |
| 英文摘要 | The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field. The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment. Methodologically, tree-based ensembles and deep learning models dominate the literature, with SHapley Additive exPlanations (SHAP) frequently adopted as the principal post-hoc explanation technique. More importantly, the review further documents how the role of XAI has shifted: rather than being used solely as a tool for interpreting models after training, it is increasingly integrated into the modeling cycle itself. Recent applications include its use in feature selection, adaptive sampling strategies, and model evaluation. The evidence also shows that GeoXAI extends beyond producing feature rankings. It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms. Nevertheless, several key challenges remain unresolved within the field. These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability, the demanding scholarly task of reliably distinguishing correlation from causation, and the development of appropriate methods for the treatment of complex spatio-temporal dynamics. |
| URL标识 | 查看原文 |
| WOS研究方向 | Engineering ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:001652261500001 |
| 出版者 | TECH SCIENCE PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219413]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Tosan, Moein |
| 作者单位 | 1.Nankai Univ, Coll Environm Sci & Engn, Sino Canada Joint R&D Ctr Water & Environm Safety, Tianjin 300071, Peoples R China 2.Univ Tehran, Coll Agr & Nat Resources, Dept Irrigat & Reclamat Engn, Karaj 3158777871, Iran; 3.Univ Tabriz, Fac Civil Engn, Ctr Excellence Hydroinformat, 29 Bahman Ave, Tabriz 5166616471, Iran; 4.World Peace Univ, Fac Engn & Architecture, Dept Civil Engn, Yenisehir, Sht Kemal Ali Omer St 22,Mersin 10, Nicosia Trnc, Turkiye; 5.Lubeck Univ Appl Sci, Dept Civil Engn, D-23562 Lubeck, Germany; 6.Ilia State Univ, Dept Civil Engn, GE-0162 Tbilisi, Georgia; 7.Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 02841, South Korea; 8.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 9.Kyoto Univ, Disaster Prevent Res Inst DPRI, Kyoto 6110011, Japan; 10.Univ Calif Los Angeles UCLA, Dept Civil & Environm Engn, Los Angeles, CA 90095 USA; |
| 推荐引用方式 GB/T 7714 | Tosan, Moein,Nourani, Vahid,Kisi, Ozgur,et al. The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence[J]. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,4602,N/A. |
| APA | Tosan, Moein.,Nourani, Vahid.,Kisi, Ozgur.,Zhang, Yongqiang.,Kantoush, Sameh A..,...&Huang, Jinhui Jeanne.(4602).The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence.CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES,N/A. |
| MLA | Tosan, Moein,et al."The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence".CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES N/A(4602). |
入库方式： OAI收割
来源：地理科学与资源研究所
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