Integrated assessment of flood susceptibility and population exposure using machine learning: a case study of Wajima, Japan

Information from the abstract

Floods increasingly disrupt daily life and cause substantial social and economic losses. Although flood susceptibility mapping (FSM) is widely used to identify flood-prone areas, the spatial relationship between FSM and population exposure has received relatively little attention. This study addresses this gap by integrating machine-learning-based FSM with census-based population distribution to assess population exposure in Machino district, Wajima, Ishikawa, Japan, which was severely affected by the September 2024 Noto Peninsula heavy rainfall event. Four models were compared: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Autoencoder Multilayer Perceptron (AE-MLP). Model performance was evaluated using five-fold spatial cross-validation, the area under the receiver operating characteristic curve (AUC), and other classification metrics. Mean cross-validation AUCs were 97.2% for RF, 97.1% for XGBoost, 95.4% for SVM, and 95.6% for AE-MLP. Pairwise DeLong tests using pooled out-of-fold predictions showed no significant difference between RF and XGBoost, whereas both significantly outperformed AE-MLP and SVM. The resulting FSMs consistently identified river corridors and adjacent lowlands as highly susceptible. Depending on the model, 33.34–58.08% of the population was located in moderate- to very-high-susceptibility zones. These findings underscore the importance of integrating flood susceptibility and population exposure assessments for urban planning and disaster management.

Why this record is monitored

This record has an Impact Signal of 83/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Flood Risk Assessment and Management · Landslides and related hazards · Hydrological Forecasting Using AI