| DOI: | 10.1007/s11069-020-04453-3
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| 论文题名: | Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB |
| 作者: | Norallahi M.; Seyed Kaboli H.
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| 刊名: | Natural Hazards
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| ISSN: | 0921030X
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| 出版年: | 2021
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| 卷: | 106, 期:1 | | 起始页码: | 119
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| 结束页码: | 137
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| 语种: | 英语
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| 中文关键词: | Flood susceptibility
; GIS
; Iran
; Machine learning
; Urban flood
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| 英文关键词: | developing world
; flood damage
; genetic algorithm
; GIS
; machine learning
; mapping
; maximum entropy analysis
; urban development
; Iran
; Kermanshah
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| 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. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/169261
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| Appears in Collections: | 气候变化与战略
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作者单位: | Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran
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| Recommended Citation: | |
Norallahi M.,Seyed Kaboli H.. Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB[J]. Natural Hazards,2021-01-01,106(1)
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