TY  - EJOU
AU  - Tosan, Moein 
AU  - Nourani, Vahid 
AU  - Kisi, Ozgur 
AU  - Zhang, Yongqiang 
AU  - Kantoush, Sameh A. 
AU  - Gebremichael, Mekonnen 
AU  - Taghizadeh-Mehrjardi, Ruhollah 
AU  - Huang, Jinhui Jeanne 

TI  - The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 1
SN  - 1526-1506

AB  - 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<b>)</b> 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.
KW  - Explainable artificial intelligence (XAI); geohazard assessment; machine learning; SHAP; trustworthy AI; model interpretability

DO  - 10.32604/cmes.2025.074768