{"title":"Research progress and prospects of urban flooding simulation: From traditional numerical models to deep learning approaches","authors":"Bowei Zeng , Guoru Huang , Wenjie Chen","doi":"10.1016/j.envsoft.2024.106213","DOIUrl":null,"url":null,"abstract":"<div><p>The rise in urban flooding events poses a threat to public safety, property, and economic stability. To prevent urban flooding and manage stormwater effectively, relying solely on engineering solutions is insufficient. Therefore, it is critical to implement non-engineering measures such as urban flood warnings and forecasting. This article reviews the characteristics of different urban flood models based on different hydrological and hydrodynamic principles and deep learning (DL). It highlights the limitations of coupled hydrological-hydrodynamic models in terms of timeliness. Additionally, it discusses research on the use of Numerical Simulation in hydrological early warning and forecasting. Compared to traditional hydrodynamic models that rely on physical mechanisms, models driven by DL methods can effectively and adaptively extract input-output relationships of complex systems. Subsequently, a summary of the current flood models is presented, followed by a discussion of future development trends and challenges.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106213"},"PeriodicalIF":4.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815224002743","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The rise in urban flooding events poses a threat to public safety, property, and economic stability. To prevent urban flooding and manage stormwater effectively, relying solely on engineering solutions is insufficient. Therefore, it is critical to implement non-engineering measures such as urban flood warnings and forecasting. This article reviews the characteristics of different urban flood models based on different hydrological and hydrodynamic principles and deep learning (DL). It highlights the limitations of coupled hydrological-hydrodynamic models in terms of timeliness. Additionally, it discusses research on the use of Numerical Simulation in hydrological early warning and forecasting. Compared to traditional hydrodynamic models that rely on physical mechanisms, models driven by DL methods can effectively and adaptively extract input-output relationships of complex systems. Subsequently, a summary of the current flood models is presented, followed by a discussion of future development trends and challenges.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.