TY  - JOUR
N1  - wpt2023088
ID  - SisLab4811
UR  - https://iwaponline.com/wpt/article/doi/10.2166/wpt.2023.088/95482
A1  - Vu, Van Tich
A1  - Nguyen, Huu Duy
A1  - Vu, Phuong Lan
A1  - Ha, Minh Cuong
A1  - Bui, Van Dong
A1  - Nguyen, Thi Oanh
A1  - Hoang, Van Hiep
A1  - Nguyen, Thanh Kim Hue
Y1  - 2023/06/02/
N2  - Flood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objective of this study was to evaluate the effects of climate and land use change on flood susceptibility in Thua Thien Hue province, Vietnam, using machine learning techniques (support vector machine (SVM) and random forest (RF)) and remote sensing. The machine learning models used a flood inventory including 1,864 flood locations and 11 conditional factors in 2017 and 2021, as the input data. The predictive capacity of the proposed models was assessed using the area under the curve (AUC), the root mean square error (RMSE), and the mean absolute error (MAE). Both proposed models were successful, with AUC values exceeding 0.95 in predicting the effects of climate and land use change on flood susceptibility. The RF model, with AUC = 0.98, outperformed the SVM model (AUC = 0.97). The areas most susceptible to flooding increased between 2017 and 2021 due to increased built-up area. The results of the study confirm machine learning's capacity to assess differences in flood susceptibility.
PB  - IWA publishing
JF  - Water Practice and Technology
TI  - Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam
AV  - public
ER  -