A novel flood risk management approach based on future climate and land use change scenarios

NGUYEN, Huu Duy; NGUYEN, Quoc-Huy; DANG, Dinh Kha; VAN, Chien Pham; TRUONG, Quang Hai; PHAM, Si Dung; BUI, Quang-Thanh; PETRISOR, Alexandru-Ionut

dc.contributor.author	NGUYEN, Huu Duy
dc.contributor.author	NGUYEN, Quoc-Huy
dc.contributor.author	DANG, Dinh Kha
dc.contributor.author	VAN, Chien Pham
dc.contributor.author	TRUONG, Quang Hai
dc.contributor.author	PHAM, Si Dung
dc.contributor.author	BUI, Quang-Thanh
dc.contributor.author	PETRISOR, Alexandru-Ionut
dc.date.accessioned	2024-06-13T11:05:15Z
dc.date.available	2024-06-13T11:05:15Z
dc.date.issued	2024
dc.identifier.citation	NGUYEN, Huu Duy et al. A novel flood risk management approach based on future climate and land use change scenarios. In: Science of The Total Environment, 2024, vol. 921, pp. 171204. ISSN 0048-9697.	en_US
dc.identifier.issn	0048-9697
dc.identifier.uri	https://doi.org/10.1016/j.scitotenv.2024.171204
dc.identifier.uri	http://repository.utm.md/handle/5014/27390
dc.description	Acces full text - https://doi.org/10.1016/j.scitotenv.2024.171204	en_US
dc.description.abstract	This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding.	en_US
dc.language.iso	en	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartofseries	Science of The Total Environment;2024, vol. 921
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	flooding risks	en_US
dc.subject	machine learning	en_US
dc.subject	hydraulic modeling	en_US
dc.subject	Vietnam	en_US
dc.title	A novel flood risk management approach based on future climate and land use change scenarios	en_US
dc.type	Article	en_US