eprintid: 4811
rev_number: 6
eprint_status: archive
userid: 422
dir: disk0/00/00/48/11
datestamp: 2023-06-15 04:10:23
lastmod: 2023-06-15 04:10:23
status_changed: 2023-06-15 04:10:23
type: article
metadata_visibility: show
creators_name: Vu, Van Tich
creators_name: Nguyen, Huu Duy
creators_name: Vu, Phuong Lan
creators_name: Ha, Minh Cuong
creators_name: Bui, Van Dong
creators_name: Nguyen, Thi Oanh
creators_name: Hoang, Van Hiep
creators_name: Nguyen, Thanh Kim Hue
title: Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam
ispublished: pub
subjects: Aerospace
divisions: sae
note: wpt2023088
abstract: 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.
date: 2023-06-02
date_type: published
publisher: IWA publishing
official_url: https://iwaponline.com/wpt/article/doi/10.2166/wpt.2023.088/95482
id_number: https://doi.org/10.2166/wpt.2023.088
full_text_status: public
publication: Water Practice and Technology
refereed: TRUE
citation:   Vu, Van Tich and Nguyen, Huu Duy and Vu, Phuong Lan and Ha, Minh Cuong and Bui, Van Dong and Nguyen, Thi Oanh and Hoang, Van Hiep and Nguyen, Thanh Kim Hue  (2023) Predicting land use effects on flood susceptibility using machine learning and remote sensing in coastal Vietnam.  Water Practice and Technology .        
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4811/1/wpt2023088.pdf