Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method

Abstract

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.

Keywords:

• Ensemble method
• flash flood susceptibility mapping
• urban flood
• genetic algorithm

Author’s contributions

Azlan Saleh, Ali Yuzir: Conceptualization, Writing- original draft, Software, Formal analysis, Visualization. Nuridah Sabtu: Formal analysis; Writing- original draft, Visualization. Sohaib K. M. Abujayyab, Mudashiru Rofiat Bunmi, Quoc Bao Pham: Data curation, Software, Writing, Review and editing.

Acknowledgments

The first author would like to acknowledge Malaysia-Japan International Institute of Technology (MJIIT), UT M for providing Incentive Scheme to undertake his PhD program. The authors would also like to thanks the MBP P, DID, JMG, P LANMalaysia and P EGIS, for the data used in this research.

Disclosure statement

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. There are no conflicts of interest to declare.

Availability of data and materials

The data that support the findings of this study are available from the author, [Azlan Saleh, [email protected]], upon reasonable request.