DOI: 10.1016/j.scitotenv.2020.137077
论文题名: Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse
作者: Wu Z. ; Zhou Y. ; Wang H. ; Jiang Z.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 716 语种: 英语
英文关键词: Data warehouse
; Deep learning
; Depth prediction
; Urban flood
Scopus关键词: Catchments
; Climate change
; Data warehouses
; Decision trees
; Deep learning
; Flood control
; Floods
; Forecasting
; Land use
; Learning algorithms
; Rain
; Regression analysis
; Global climate changes
; Gradient boosting
; Rainfall duration
; Rainfall return periods
; Rapid urbanization process
; Urban flood control
; Urban floods
; Water accumulation
; Urban growth
; rain
; algorithm
; drainage
; flood control
; machine learning
; prediction
; rainfall
; return period
; urban area
; algorithm
; Article
; catchment area
; China
; data warehouse
; deep learning
; evaporation
; flooding
; land use
; measurement accuracy
; permeability
; prediction
; priority journal
; risk assessment
; statistical model
; urban area
; validity
; China
; Henan
; Zhengzhou
英文摘要: With the global climate change and the rapid urbanization process, there is an increase in the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the depth prediction of urban flood is very important for urban flood control. In this study, an urban flood data warehouse was established with available structured and unstructured urban flood data. In this study, an urban flood data warehouse was established with available structured and unstructured urban flood data. Based on this, a regression model to predict the depth of urban flooded areas was constructed with deep learning algorithm, named Gradient Boosting Decision Tree (GBDT). The flood condition factors used in modeling were rainfall, rainfall duration, peak rainfall, evaporation, land use (the proportion of roads, woodlands, grasslands, water bodies and building), permeability, catchment area, and slope. Based on the rainfall data of different rainfall return periods, flood condition maps were produced using GIS. In addition, the feature importance of these conditioning factors was determined based on the regression model. The results demonstrated that the growth rate of the number and depth of the water accumulation points increased significantly after the rainfall return period of ‘once in every two years’ in Zhengzhou City, and the flooded areas mainly occurred in the old urban areas and parts of southern Zhengzhou. The relative error of prediction results was 11.52%, which verifies the applicability and validity of the method in the depth prediction of urban floods. The results of this study can provide a scientific basis for urban flood control and drainage. © 2020 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158221
Appears in Collections: 气候变化与战略
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作者单位: College of Water Conservancy Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China
Recommended Citation:
Wu Z.,Zhou Y.,Wang H.,et al. Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse[J]. Science of the Total Environment,2020-01-01,716