Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Flooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e. Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development.
Açıklama
Anahtar Kelimeler
Ensemble method, flash flood susceptibility mapping, urban flood, genetic algorithm
Kaynak
Geocarto International
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
37
Sayı
25
