A novel combination of deep neural network and Manta ray foraging optimization for flood susceptibility mapping in Quang Ngai province, Vietnam

Abstract

Floods are the most dangerous natural disasters globally, occurring on a large scale, and cause significant economic and environmental damage. Therefore, determining flood susceptibility is essential to reducing the flood effects on human lives and materials. The main objective of this research is to develop a novel hybrid algorithm, through combining deep neural network and Manta ray foraging optimization (DNN-MRFO), to generate flood susceptibility map for Quang Ngai province, Vietnam. A geospatial distribution analytical approach was used to generate input data, including 2176 flood locations points and 13 influencing factors. A comparative analysis of the proposed model with five models namely DNN – particle swarm optimization (DNN-PSO), DNN – grey wolf optimization (DNN-GWO), DNN – social spider optimization (DNN-SSO), support vector machine (SVM), gradient boosting regression (GBR) was carried out using different evaluation indices. The result shows that combining DNN and MRFO improved flood susceptibility classification precision with an area under the curve (AUC) of 0.98. The findings of this study are significant for supporting policymakers in understanding and identifying issues, which support improve their adaptation strategies.

Keywords:

• Flood susceptibility
• deep neural network
• Manta Ray foraging optimization
• Quang Ngai

Acknowledgements

This research is part of Quan Vu Viet Du’s Ph.D. dissertation. The authors thank all anonymous reviewers for their critical and constructive comments, which have improved the quality of the manuscript.

Disclosure statement

We have no conflict of interest in this article.

Additional information

Funding

This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.20.12.