A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Authors:Khosravi, KhabatShahabi, HimanBinh Thai PhamAdamowski, JanShirzadi, AtaollahPradhan, BiswajeetDou, JieLy, Hai-BangGrof, GyulaHuu Loc HoHong, HaoyuanChapi, KamranPrakash, Indra

Source:JOURNAL OF HYDROLOGY

Volume:573

DOI:10.1016/j.jhydrol.2019.03.073

Published:2019

Document Type:Article

Abstract:Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.

Author Information

Corresponding Author:

Reprint Address:Pham, BT (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam.; Dou, J (corresponding author), PWRI, Tsukuba, Ibaraki, Japan.; Ho, HL (corresponding author), Nguyen Tat Thanh Univ, NTT Hitech Inst, Ho Chi Minh City, Vietnam.; Hong, H (corresponding author), Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China.

Addresses:[Khosravi, Khabat] Sari Agr Sci & Nat Resources Univ, Dept Watershed Management Engn, Sari, Iran. [Shahabi, Himan] Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran. [Binh Thai Pham] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam. [Adamowski, Jan] McGill Univ, Dept Bioresource Engn, Ste Anne De Bellevue, PQ, Canada. [Shirzadi, Ataollah; Chapi, Kamran] Univ Kurdistan, Dept Rangeland & Watershed Management, Fac Nat Res, Sanandaj, Iran. [Pradhan, Biswajeet] Univ Technol Sydney, Fac Engn & IT, CAMGIS, Sydney, NSW 2007, Australia. [Pradhan, Biswajeet] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 NeungdongroGwangjin Gu, Seoul 05006, South Korea. [Dou, Jie] PWRI, Tsukuba, Ibaraki, Japan. [Ly, Hai-Bang] Univ Transport Technol, Hanoi 100000, Vietnam. [Grof, Gyula] Budapest Univ Technol & Econ, Dept Energy Engn, Budapest, Hungary. [Huu Loc Ho] Nguyen Tat Thanh Univ, NTT Hitech Inst, Ho Chi Minh City, Vietnam. [Hong, Haoyuan] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China. [Prakash, Indra] Govt Gujarat, Dept Sci & Technol, BISAG, Gandhinagar, India.

E-mail Addresses:phambinhgtvt@gmail.com; douj888@gmail.com; huuloc20686@gmail.com; 171301013@stu.njnu.edu.cn

Note
The information comes from publicly available information on the Internet.
This service only provides full-text access links. And the specific full-text access permissions depend on whether articles are OA resources or user IP permissions.