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A Systematic Comparison of Statistical and Machine-Learning Models for Mapping Landslide Susceptibility: Evidence from the 2018 Rainfall-Induced Landslides in Hiroshima
https://nied-repo.bosai.go.jp/records/7590
https://nied-repo.bosai.go.jp/records/7590ee949afb-5078-4038-b68c-d2476471742e
| Item type | researchmap(1) | |||||||||||||
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| 公開日 | 2026-07-27 | |||||||||||||
| タイトル | ||||||||||||||
| 言語 | en | |||||||||||||
| タイトル | A Systematic Comparison of Statistical and Machine-Learning Models for Mapping Landslide Susceptibility: Evidence from the 2018 Rainfall-Induced Landslides in Hiroshima | |||||||||||||
| 著者 |
Kumari Kanchana Mallika Achchillage
× Kumari Kanchana Mallika Achchillage
× Tsuyoshi Wakatsuki
× Chiaki T. Oguchi
× Masahiko Osada
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | Landslide susceptibility mapping (LSM) is an essential tool for hazard assessment and land-use planning in landslide-prone areas. This study compares three statistical models—Frequency Ratio (FR), Weight of Evidence (WoE), and Logistic Regression (LR)—with six machine-learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Decision Tree (DT), for regional landslide susceptibility assessment in Hiroshima Prefecture, Japan. A balanced dataset comprising 1936 landslide and 1936 non-landslide samples was developed from the 2018 rainfall-induced landslide inventory, utilizing seven conditioning factors: slope angle, profile curvature, aspect, elevation, lithology, soil water index, and 24 h cumulative rainfall. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Among the statistical models, WoE exhibited the highest performance, while SVM provided the most balanced results among the machine-learning models. Both modeling approaches consistently identified lithology and slope angle as the primary controls on landslide occurrence. Independent validation demonstrated comparable predictive performance for both models; however, spatial validation showed that WoE assigned 96.72% of observed landslides to the High and Very High susceptibility classes, compared to 72.54% for SVM. These findings underscore the importance of integrating conventional classification metrics with spatial validation to enhance the evaluation and interpretation of landslide susceptibility models for regional hazard assessment. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : GeoHazards 巻 7, 号 3, p. 87-87, 発行日 2026-07-18 |
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| 言語 | en | |||||||||||||
| 出版者 | MDPI AG | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2624-795X | |||||||||||||
| DOI | ||||||||||||||
| 関連識別子 | 10.3390/geohazards7030087 | |||||||||||||
