气候变化领域集成服务门户(CCPortal): Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB

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DOI	10.1007/s11069-020-04453-3
	Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB
	Norallahi M.; Seyed Kaboli H.
发表日期	2021
ISSN	0921030X
起始页码	119
结束页码	137
卷号	106 期号:1
英文摘要	Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. © 2021, Springer Nature B.V.
关键词	Flood susceptibility GIS Iran Machine learning Urban flood
英文关键词	developing world; flood damage; genetic algorithm; GIS; machine learning; mapping; maximum entropy analysis; urban development; Iran; Kermanshah
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206462
作者单位	Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran
推荐引用方式 GB/T 7714	Norallahi M.,Seyed Kaboli H.. Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB[J],2021,106(1).
APA	Norallahi M.,&Seyed Kaboli H..(2021).Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB.Natural Hazards,106(1).
MLA	Norallahi M.,et al."Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB".Natural Hazards 106.1(2021).