11 citations as recorded by crossref.

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2. Integrating machine learning algorithms and fuzzy AHP for assessing livelihood vulnerability in Southwestern Coastal Bangladesh A. Tasnuva & Q. Bari https://doi.org/10.1016/j.rsma.2024.103825
3. Application of Machine Learning for Integrated Flood Risk Assessment: Case Study of Hurricane Harvey in Houston, Texas https://doi.org/10.20900/jsr20260025
4. Predicting Archaeological Site Vandalism Risk in Pakistan Using GIS-Based Accessibility Indicators and Socio-Demographic Machine Learning Models S. Ullah et al. https://doi.org/10.1080/13505033.2026.2665274
5. Developing a disaster risk index for coastal communities in southwest Bangladesh: Shifting from data-driven models to holistic approaches A. Tasnuva et al. https://doi.org/10.1016/j.ecolind.2024.112381
6. Artificial intelligence-driven approaches for assessing social vulnerability to natural hazards: a comprehensive bibliometric review P. Goktas & H. Rabiei-Dastjerdi https://doi.org/10.1007/s11069-025-07808-w
7. AI-Driven Multi-Model Classification of Rural Settlements for Targeted Rural Revitalization: A Case Study of Gaoqing County, Shandong Province, China J. He et al. https://doi.org/10.3390/land14122298
8. Geospatial Model Optimization for Mapping Social Vulnerability to Natural Disasters Using Fuzzy Geographically Weighted Clustering and Flower Pollination Algorithm D. Istiawan et al. https://doi.org/10.59395/ijadis.v6i3.1452
9. Analyzing Susceptibility to Tornado-Induced Injuries Using Hybrid Tree-Based Classifiers and Advanced Resampling Techniques R. Özgenç et al. https://doi.org/10.1007/s13351-025-5055-8
10. Enhancing the reliability of social vulnerability assessment to natural hazards through combined machine learning methods: A case study in Vietnam T. Nguyen & N. Le https://doi.org/10.1016/j.nhres.2025.09.004
11. Applications and challenges of machine learning in urban thermal environment: A systematic review Y. Jiang et al. https://doi.org/10.1016/j.scs.2026.107400