Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques

JOURNAL OF HYDROLOGY

卷:569

Article

Darabi, Hamid¹  Choubin, Bahram²  Rahmati, Omid³  Haghighi, Ali Torabi¹  Pradhan, Biswajeet^4,5  Klove, Bjorn¹ 

[1] Univ Oulu, Water Resources & Environm Engn, POB 4300, FIN-90014 Oulu, Finland

[2] Univ Tehran, Fac Nat Resources, Dept Reclamat Arid & Mt Reg, Karaj, Iran

[3] Islamic Azad Univ, Khorramabad Branch, Young Researchers & Elites Club, Khorramabad, Iran

[4] Univ Technol Sydney, Ctr Adv Modelling & Geospatial Informat Syst, Sch Informat Syst & Modelling, Fac Engn & IT, Sydney, NSW 2007, Australia

[5] Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea

关键词: Urban planning;    Flood risk management;    GIS;    FANP;    Data-mining;   

DOI  :  10.1016/j.jhydrol.2018.12.002

来源: Elsevier

【 摘 要 】

Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.

【 授权许可】

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