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Urban Flood Modelling and Risk Management
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Urban Flood Modelling and Risk Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 3201

Special Issue Editors

Dr. Huabing Huang

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
Interests: urban flood modelling; urban hydrology; urban resilience; machine learning; spatial analysis; terrian anlysis
Special Issues, Collections and Topics in MDPI journals
Dr. Hongshi Xu

E-Mail Website
Guest Editor
Yellow River Laboratory, Zhengzhou University, Zhengzhou, China
Interests: urban flood modelling; urban driange design; compound flood; machine learing
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Zhong

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
Interests: climate changes and extreme events; disaster resilence; hydrological remote sensing; hydrological model

Special Issue Information

Dear Colleagues,

Urban flood and its risks have been changing in pattern, mechanism, and intensity due to the interaction of warming climate, rapid urbanization, and mitigation measures. Understanding these changes in urban flood risk relies on observation and modelling, which present challenges in urban areas where the mixture of natural and artificial landscapes is highly heterogenous over space. Recent developments on data acquisition and machine learning technique provide more physical-based, simplified, and data-driven opportunities on improving urban flood modelling, and further  enhance urban resilience to flood.

We welcome submissions that contribute, but are not limited to, the following topics:

  1. Urban flood mechanisms;
  2. Urban flood risk assessment;
  3. Data-driven flood modelling;
  4. Social sensing on urban flood;
  5. Urban flood in underground spaces;
  6. Urban flood resilience;
  7. Urban drainage design;
  8. Low impact development and sponge city.

This Special Issue particularly encourages papers that integrate machine learning with a hydrodynamic model.

Dr. Huabing Huang
Dr. Hongshi Xu
Dr. Ming Zhong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • flood risk assessment
  • flood resilience
  • extreme events
  • underground spaces
  • compound flood
  • flash flood
  • machine learning
  • social sensing

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Published Papers (2 papers)

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Research

17 pages, 11258 KiB  
Article
Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques
by Ya Chu, Weifeng Song and Dongbin Chen
Water 2024, 16(11), 1556; https://doi.org/10.3390/w16111556 - 29 May 2024
Cited by 1 | Viewed by 1122
Abstract
Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on [...] Read more.
Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction. Full article
(This article belongs to the Special Issue Urban Flood Modelling and Risk Management)
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25 pages, 20123 KiB  
Article
Monitoring of Levee Deformation for Urban Flood Risk Management Using Airborne 3D Point Clouds
by Xianwei Wang, Yidan Wang, Xionghui Liao, Ying Huang, Yuli Wang, Yibo Ling and Ting On Chan
Water 2024, 16(4), 559; https://doi.org/10.3390/w16040559 - 12 Feb 2024
Viewed by 1404
Abstract
In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can [...] Read more.
In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. Full article
(This article belongs to the Special Issue Urban Flood Modelling and Risk Management)
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