Smart Geospatial Analytics for Maladaptation Hotspot Detection: Integrating Trajectory Classification, Random Forest, and SHAP
不適応ホットスポット検出のためのスマート地理空間分析：軌跡分類、ランダムフォレスト、SHAPの統合 (AI 翻訳)
Nutchanat Buasri, P. Littidej, Benjamabhorn Pumhirunroj, Kaveepoj Banluewong, Donald Slack
🤖 gxceed AI 要約
日本語
タイの洪水常襲地域を対象に、人口増加と洪水頻度の関係を分析。繰り返し洪水地域でも一部で人口増加（不適応）が見られ、ランダムフォレストとSHAPで洪水頻度が主要因と特定。再建期間が介入の重要時期と示唆。
English
This study analyzes population dynamics in flood-prone Thailand, finding that despite repeated flooding, some areas show late population growth (maladaptation). Using Random Forest and SHAP, flood frequency is the dominant predictor, with thresholds for hotspot identification. The reconstruction period is highlighted as a critical intervention window.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では気候変動適応策や防災計画に示唆を与える。特に、災害リスク評価と土地利用規制の連携は、今後の国土強靱化政策に参考となる。
In the global GX context
Globally, this contributes to climate adaptation and disaster risk reduction literature, offering a replicable ML pipeline for identifying maladaptation hotspots, relevant for climate-resilient development planning.
👥 読者別の含意
🔬研究者:Provides a novel method combining trajectory classification and interpretable ML for disaster research.
🏢実務担当者:Offers actionable thresholds for early warning and zoning restrictions in flood-prone areas.
🏛政策担当者:Highlights the need for targeted relocation and reconstruction-period interventions.
📄 Abstract（原文）
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, 2018–2024), 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 >5% only after 2022 floods), Mann–Whitney U tests to compare growth rates, and Random Forest with SHAP analysis to identify predictors of maladaptation hotspots. Repeatedly flooded areas (≥2 flood events out of 3 observation years) exhibited significantly lower median growth than non-repeatedly flooded areas (−0.386 vs. −0.200; 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 ≥ 3 and DEM < 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—trajectory classification combined with interpretable machine learning—offers a replicable approach for smart geospatial analytics in disaster research.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/s26154853first seen 2026-08-09 05:53:47
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