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Urban Flood Model Developments and Flood Forecasting
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Urban Flood Model Developments and Flood Forecasting

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 16725

Special Issue Editors

Prof. Dr. Jorge Leandro

E-Mail Website
Guest Editor
Hydromechanik and Hydraulic Engineering, University of Siegen, Siegen, Germany
Interests: urban hydrology; urban resilience; rainfall-runoff modelling; flood inundation modelling; flood forecasting; calibration
Special Issues, Collections and Topics in MDPI journals
Dr. Mingfu Guan

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, China
Interests: hydro-system modelling; urban hydrology; sustainable urban water system; flood modelling; computational hydraulics; river and coastal hydraulics; sediment dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Flood poses a severe threat to urban critical infrastructure. Flood forecasting can contribute to disaster risk reduction as it is an important and integral part of flood management strategies. However, there are significant technical challenges associated with providing timely flood warning with enough lead time, and the accurate representation of the numerous complex physical and hydrodynamic processes involved in urban flooding is also still a challenge.

The aim of this Special Issue is thus to publish the latest advances and developments concerning the modeling and forecasting of flooding in urban areas and contribute to our scientific understanding and offer improved techniques to reduce flood risk.

It is anticipated that this issue will contain contributions on novel methodologies including (but not limited to) flood forecasting methods, data handling techniques, experimental research in urban drainage, and/or sustainable drainage systems and novel numerical approaches.

We further encourage the submission of original research, synthetic reviews or case study papers applying numerical or experimental modeling techniques in order to study the following topics:

  • Flood forecasting;
  • Shallow overland flows over urban terrains;
  • Management of urban flood risk;
  • Drainage system/surface flow interactions;
  • Calibration and validation;
  • Uncertainty quantification.

The accepted papers will be published as open access ensuring widespread availability.

Prof. Dr. Jorge Leandro
Dr. Mingfu Guan
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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14 pages, 2382 KiB  
Article
Fast Prediction of Urban Flooding Water Depth Based on CNN−LSTM
by Jian Chen, Yaowei Li and Shanju Zhang
Water 2023, 15(7), 1397; https://doi.org/10.3390/w15071397 - 4 Apr 2023
Cited by 12 | Viewed by 3127
Abstract
Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine [...] Read more.
Rapid prediction of urban flooding is an important measure to reduce the risk of flooding and to protect people’s property. In order to meet the needs of emergency flood control, this paper constructs a rapid urban flood prediction model based on a machine learning approach. Using the simulation results of the hydrodynamic model as the data driver, a neural network structure combining convolutional neural network (CNN) and long and short-term memory network (LSTM) is constructed, taking into account rainfall factors, geographical data, and the distribution of the drainage network. The study was carried out with the central city of Zhoukou as an example. The results show that after the training of the hydrodynamic model and CNN−LSTM neural network model, it can quickly predict the depth of urban flooding in less than 10 s, and the average error between the predicted depth of flooding and the measured depth of flooding does not exceed 6.50%, which shows that the prediction performance of the neural network is good and can meet the seeking of urban emergency flood control and effectively reduce the loss of life and property. Full article
(This article belongs to the Special Issue Urban Flood Model Developments and Flood Forecasting)
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28 pages, 15267 KiB  
Article
A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation
by Yanxia Shen, Chunbo Jiang, Qi Zhou, Dejun Zhu and Di Zhang
Water 2021, 13(23), 3454; https://doi.org/10.3390/w13233454 - 5 Dec 2021
Cited by 4 | Viewed by 2336
Abstract
Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave [...] Read more.
Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management. Full article
(This article belongs to the Special Issue Urban Flood Model Developments and Flood Forecasting)
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20 pages, 10607 KiB  
Article
Inclusion of Narrow Flow Paths between Buildings in Coarser Grids for Urban Flood Modeling: Virtual Surface Links
by Sebastian Ramsauer, Jorge Leandro and Qing Lin
Water 2021, 13(19), 2629; https://doi.org/10.3390/w13192629 - 24 Sep 2021
Cited by 5 | Viewed by 2389
Abstract
Urban flood modeling benefits from new remote sensing technologies, which provide high-resolution data and allow the consideration of small-scale urban key features. Since high-resolution data often result in large simulation runtimes, coarsening of the 2D grid via resampling techniques can be used to [...] Read more.
Urban flood modeling benefits from new remote sensing technologies, which provide high-resolution data and allow the consideration of small-scale urban key features. Since high-resolution data often result in large simulation runtimes, coarsening of the 2D grid via resampling techniques can be used to achieve a good balance between accuracy and computation time. However, the representation of urban features and topographical properties degrades, since small-scale features are blurred. Therefore, narrow flow paths between buildings are often not considered, building’s sizes are overestimated, and their arrangement in the grid changes. Thus, flow paths change and waterways are blocked, leading to incorrect inundations around buildings. This paper develops a method to improve the simulation results of coarser grids by adding virtual surface links (VSL) between buildings. The VSL mimic the flow paths of a high-resolution model in the areas of interest. The approach is developed for dual-drainage 1D/2D models. The approach shows a visible improvement at the localized level where the VSL are applied, in terms of under/overestimating flooding and a moderate overall improvement of the simulation results. Relatively to the model resolution of 2 m, the computational time, by applying this method, is reduced by 93.6% when using a 5 m grid and by 99% when using a 10 m grid. For a small test case, where the local effects are investigated, the error in the maximum water volume, relative to a grid size of 2 m, is reduced from 69.63% to 5.03% by using a 5 m grid and from 152.75% to 22.92% for a 10 m grid. Full article
(This article belongs to the Special Issue Urban Flood Model Developments and Flood Forecasting)
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15 pages, 6966 KiB  
Article
Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts
by Hongfei Zhu, Jorge Leandro and Qing Lin
Water 2021, 13(16), 2252; https://doi.org/10.3390/w13162252 - 18 Aug 2021
Cited by 13 | Viewed by 3397
Abstract
Flooding is the world’s most catastrophic natural event in terms of losses. The ability to forecast flood events is crucial for controlling the risk of flooding to society and the environment. Artificial neural networks (ANN) have been adopted in recent studies to provide [...] Read more.
Flooding is the world’s most catastrophic natural event in terms of losses. The ability to forecast flood events is crucial for controlling the risk of flooding to society and the environment. Artificial neural networks (ANN) have been adopted in recent studies to provide fast flood inundation forecasts. In this paper, an existing ANN trained based on synthetic events was optimized in two directions: extending the training dataset with the use of hybrid dataset, and selection of the best training function based on six possible functions, namely conjugate gradient backpropagation with Fletcher–Reeves updates (CGF) with Polak–Ribiére updates (CGP) and Powell–Beale restarts (CGB), one-step secant back-propagation (OSS), resilient backpropagation (RP), and scaled conjugate gra-dient backpropagation (SCG). Four real flood events were used to validate the performance of the improved ANN over the existing one. The new training dataset reduced the model’s rooted mean square error (RMSE) by 10% for the testing dataset and 16% for the real events. The selection of the resilient backpropagation algorithm contributed to 15% lower RMSE for the testing dataset and up to 35% for the real events when compared with the other five training functions. Full article
(This article belongs to the Special Issue Urban Flood Model Developments and Flood Forecasting)
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Review

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23 pages, 2292 KiB  
Review
Applications of Advanced Technologies in the Development of Urban Flood Models
by Yuna Yan, Na Zhang and Han Zhang
Water 2023, 15(4), 622; https://doi.org/10.3390/w15040622 - 5 Feb 2023
Cited by 6 | Viewed by 3877
Abstract
Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation [...] Read more.
Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system. Full article
(This article belongs to the Special Issue Urban Flood Model Developments and Flood Forecasting)
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