Data-driven surrogate modeling: Introducing spatial lag to consider spatial autocorrelation of flooding within urban drainage systems

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成果类型：

期刊论文

作者：

Li, Heng;Zhang, Chunxiao;Chen, Min;Shen, Dingtao;Niu, Yunyun

通讯作者：

Zhang, Chunxiao(zcx@cugb.edu.cn)

作者机构：

[Zhang, Chunxiao; Li, Heng; Niu, Yunyun] China Univ Geosci Beijing, Sch Informat Engn, 29, Xueyuan Rd, Beijing 100083, Peoples R China.

[Zhang, Chunxiao] Minist Nat Resources, Observat & Res Stn Beijing Fangshan Comprehens Exp, Beijing 100083, Peoples R China.

[Chen, Min] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.

[Shen, Dingtao] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.

[Shen, Dingtao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.

通讯机构：

[Chunxiao Zhang] S

School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing, 100083, China<&wdkj&>Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources, Beijing, 100083, China

语种：

英文

关键词：

Machine learning-based surrogate modeling (MLSM);Spatial autocorrelation;Spatial lag;Storm water management model(SWMM);Urban flooding simulation

期刊：

Environmental Modelling & Software

ISSN：

1364-8152

年：

2023

卷：

161

页码：

105623

DOI：

10.1016/j.envsoft.2023.105623

基金类别：

This work was supported by the National Natural Science Foundation of China (No. 42077438 ) and the Fundamental Research Funds for the Central Universities (No. 2652018082 ). This work was supported by the National Natural Science Foundation of China (No. 61872325 , 62172373 ). The data support from the Nanchong Meteorological Bureau is gratefully acknowledged.

机构署名：

本校为其他机构

院系归属：

城市与环境科学学院

摘要：

Data-driven surrogate modeling has been increasingly employed for flooding simulation of urban drainage systems (UDSs) due to its high computational efficiency and accuracy. However, spatial autocorrelation is prevalent in many typical scenarios, including the UDS. This omission of spatial information is very likely to cause the machine learning model to capture the wrong UDS overflow mechanism from the data. To capture the spatial autocorrelation, an artificial neural network (ANN)-based surrogate modeling method that introduces spatial lag to...

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Data-driven surrogate modeling: Introducing spatial lag to consider spatial autocorrelation of flooding within urban drainage systems

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