Flood susceptibility prediction and adaptive capacity of community-based machine learning and socioeconomic data: Case study in Da Nang city, Vietnam

Duy Huu Nguyen; Thi Sen Tran; Quang- Hai Truong

doi:10.15625/2615-9783/24463

Author affiliations

Authors

Huu Duy Nguyen Faculty of Geography, University of Science, Vietnam National University, Ha Noi, Hanoi, Vietnam
Thi Sen Tran Faculty of Geography, University of Science, Vietnam National University, Ha Noi, Hanoi, Vietnam
Quang-Hai Truong Institute of Vietnamese Studies & Development Sciences, Vietnam National University (VNU), Hanoi 10000, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/24463

Keywords:

Flood susceptibility, adaptation capacity, Da Nang, Vietnam

Abstract

This study aims to predict flood susceptibility and community adaptation capacity based on machine learning and socioeconomic data. Da Nang City was selected as the case study in this study. Five machine learning algorithms, Random Forest (RF), Adaboost (ADB), Bagging (BA), Gradient Boosting (GB), and XGBoost (XGB) were used to predict flood susceptibility, and 80 households were selected to assess the community’s adaptation capacity. The findings indicate that the RF model performed better than the other models, with an AUC score of 0.989, followed by ADB (0.987), BA (0.985), XGB (0.984), and GB (0.983). The eastern regions are affected by very high and high flooding, including Hai Chau, Thanh Khe, Ngu Hanh Son districts, and part of Son Tra. These regions have low elevations and high construction density. However, western mountainous areas, such as the Hoa Vang district and part of the Lien Chieu district, are in very low- and low-flood areas. The adaptive capacity of communities in Da Nang City is shaped by natural, physical, human, social, and financial resources.

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