The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence

Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, Yongqiang Zhang⁷, Sameh A. Kantoush⁸, Mekonnen Gebremichael⁹, Ruhollah Taghizadeh-Mehrjardi¹⁰, Jinhui Jeanne Huang¹¹
1 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, 31587-77871, Iran
2 Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave, Tabriz, 51666-16471, Iran
3 Department of Civil Engineering, Faculty of Engineering and Architecture, World Peace University, Sht. Kemal Ali Omer St. No:22, Yenisehir, Nicosia/TRNC, Mersin 10, Türkiye
4 Department of Civil Engineering, Lübeck University of Applied Sciences, Lübeck, 23562, Germany
5 Department of Civil Engineering, Ilia State University, Tbilisi, 0162, Georgia
6 School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, Republic of Korea
7 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
8 Disaster Prevention Research Institute (DPRI), Kyoto University, Kyoto, 611-0011, Japan
9 Department of Civil and Environmental Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
10 Department of Soil Science and Geomorphology, University of Tübingen, Tübingen, 72076, Germany
11 College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, 300071, China
* Corresponding Author: Moein Tosan. Email: email
(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.074768

Received 17 October 2025; Accepted 16 December 2025; Published online 29 December 2025

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Abstract

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.