{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:12:26Z","timestamp":1790813546311,"version":"4.1.0"},"reference-count":26,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007288","name":"Mahasarakham University","doi-asserted-by":"publisher","award":["2-2569"],"award-info":[{"award-number":["2-2569"]}],"id":[{"id":"10.13039\/501100007288","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2-2569"],"award-info":[{"award-number":["2-2569"]}],"id":[{"id":"https:\/\/ror.org\/0453j3c58","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Flooding is among the most frequent and damaging natural hazards globally, yet the population dynamics of repeatedly flooded areas remain poorly understood, particularly the phenomenon of maladaptation population growth in hazard-prone zones despite repeated flood exposure. This study integrated annual population estimates (LandScan, 2018\u20132024), multi-year flood records (2018, 2021, 2022), and topographic variables (TWI, slope, DEM, distance to streams) across 1159 spatial units in a flood-prone region of Thailand. We employed trajectory classification to identify late_growth pixels (population increase &gt;5% only after 2022 floods), Mann\u2013Whitney U tests to compare growth rates, and Random Forest with SHAP analysis to identify predictors of maladaptation hotspots. Repeatedly flooded areas (\u22652 flood events out of 3 observation years) exhibited significantly lower median growth than non-repeatedly flooded areas (\u22120.386 vs. \u22120.200; p = 0.0092). However, 52 out of 190 repeatedly flooded pixels (27.37%) were classified as late_growth and were designated as maladaptation hotspots. Random Forest identified flood_freq as the dominant predictor (importance = 0.690), followed by DEM (0.117) and distance to streams (0.077). SHAP analysis revealed non-linear thresholds: hotspot probability increases sharply when flood_freq \u2265 3 and DEM &lt; 145 m. No significant difference in pulse_2022 was observed (p = 0.4545), indicating lagged population responses and identifying the reconstruction period as a critical intervention window. These findings challenge the assumption that repeated flooding uniformly deters settlement and provide actionable thresholds for localized early warning, zoning restrictions, and targeted relocation assistance. The methodological pipeline\u2014trajectory classification combined with interpretable machine learning\u2014offers a replicable approach for smart geospatial analytics in disaster research.<\/jats:p>","DOI":"10.3390\/s26154853","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T11:50:21Z","timestamp":1785757821000},"page":"4853","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Smart Geospatial Analytics for Maladaptation Hotspot Detection: Integrating Trajectory Classification, Random Forest, and SHAP"],"prefix":"10.3390","volume":"26","author":[{"given":"Nutchanat","family":"Buasri","sequence":"first","affiliation":[{"name":"Research Unit of Geoinformatics for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-547X","authenticated-orcid":false,"given":"Patiwat","family":"Littidej","sequence":"additional","affiliation":[{"name":"Research Unit of Geoinformatics for Spatial Management, Department of Geoinformatics, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6607-5594","authenticated-orcid":false,"given":"Benjamabhorn","family":"Pumhirunroj","sequence":"additional","affiliation":[{"name":"Program in Animal Science, Faculty of Agricultural Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaveepoj","family":"Banluewong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Informatics, Mahasarakham University, Mahasarakham 44150, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0324-2163","authenticated-orcid":false,"given":"Donald","family":"Slack","sequence":"additional","affiliation":[{"name":"Department of Civil & Architectural Engineering & Mechanics, 1209 E. Second St., P.O. Box 210072, Tucson, AZ 85721, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,1]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED) (2024). Disasters in Numbers 2023, CRED."},{"key":"ref_2","unstructured":"IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability, Cambridge University Press."},{"key":"ref_3","unstructured":"ADB (2009). The Economics of Climate Change in Southeast Asia: A Regional Review, Asian Development Bank."},{"key":"ref_4","unstructured":"World Bank (2012). Thailand Floods 2011: Rapid Assessment for Resilient Recovery and Reconstruction Planning, World Bank."},{"key":"ref_5","unstructured":"UNDRR (2022). 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