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New Methods and Technologies of Urban Flood Forecasting, Risk Assessment and Response

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1685

Special Issue Editors

Dr. Ganggang Zuo

E-Mail Website
Guest Editor
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
Interests: hydrologic forecast; process-driven modeling; data-driven modeling; machine learning; flood forecasting; runoff forecasting
Prof. Dr. Jiancang Xie

E-Mail Website
Guest Editor
State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China
Interests: intelligent water management; contingency management; decision support; comprehensive integration; urban flood management

Special Issue Information

Dear Colleagues,

Urban flooding is an increasingly severe threat to cities worldwide, leading to substantial economic losses, daily life disruptions, and serious public safety risks. The rising frequency and intensity of these events, fueled by rapid urbanization and climate change, underscore the complexities in accurately forecasting, assessing, and responding to flood risks. This Special Issue, titled "New Methods and Technologies for Urban Flood Forecasting, Risk Assessment and Response," seeks to tackle these challenges by presenting innovative research and solutions. We invite contributions exploring advanced methods and technologies for precise flood forecasting, risk assessment, and enhanced response strategies. This Special Issue invites papers that cover a broad range of topics, including, but not limited to, the following:

  • Machine learning and AI-based flood forecasting;
  • Remote sensing and GIS in flood risk assessment;
  • Innovative early warning systems;
  • Climate change impacts on urban flooding;
  • Integrated flood management and mitigation;
  • Real-time data acquisition and modeling;
  • Community-based flood response and resilience.

This Special Issue aims to provide a comprehensive platform for researchers and practitioners to share their findings and contribute to the development of more resilient urban environments.

Dr. Ganggang Zuo
Prof. Dr. Jiancang Xie
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

  • urban flood
  • flood forecasting
  • risk assessment
  • artificial intelligence
  • flood response

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Published Papers (2 papers)

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Research

18 pages, 5248 KiB  
Article
Cumulative Risk of Heavy Metals in Long-Term Operational Rain Garden
by Dandan Yan, Huaien Li, Jiake Li, Chunbo Jiang, Binkai Jia and Bo Cheng
Water 2025, 17(7), 955; https://doi.org/10.3390/w17070955 - 25 Mar 2025
Viewed by 166
Abstract
With the advancement of sponge city construction, rain gardens, as key facilities for concentrating and infiltrating rainwater runoff, have been widely established. However, the accumulation of heavy metals (HMs) in the fillers and the associated pollution risks cannot be ignored, which have a [...] Read more.
With the advancement of sponge city construction, rain gardens, as key facilities for concentrating and infiltrating rainwater runoff, have been widely established. However, the accumulation of heavy metals (HMs) in the fillers and the associated pollution risks cannot be ignored, which have a significant impact on the operational lifespan of these facilities. This study took the observation point (P) within a rain garden that has been in operation since 2012 and the control point (CK), which is the soil sample collection point in the natural infiltration area, as samples. Based on the monitoring data of HM content from 2017 to 2022, the pollution characteristics of Cu, Zn, and Cd were analyzed using enrichment factors and the geo-accumulation index, and the potential ecological risks were evaluated to reveal the impact of concentrated infiltration of runoff. The results showed that Cu and Cd accumulated in the 0–10 cm depth, while Cu and Zn exhibited seasonal annual variations, and the variation of Cd was not obvious. The study found that Cu and Zn were in a non-enriched state, while Cd was slightly enriched. Among the single ecological risk factor indices, the pollution levels of Cu and Zn were low, while that of Cd was relatively high. Comparison of the data from the observation point and the control point reveals that 88.9% of the data points of single ecological risk factor indices at each soil depth at the observation point are higher than those at the control point, revealing the impact of concentrated infiltration of rainwater runoff on the soil. However, the comprehensive assessment indicated that the overall ecological risk of the soil in the rain garden and the natural filtration area was at a low level. Nevertheless, given that the long-term operation of rain gardens may still pose pollution risks to the soil and groundwater, it is imperative to take timely measures to control HM pollution to ensure the long-term stable operation of sponge city facilities and the safety of the ecological environment. Full article
(This article belongs to the Special Issue New Methods and Technologies of Urban Flood Forecasting, Risk Assessment and Response)
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27 pages, 19056 KiB  
Article
Incorporating Recursive Feature Elimination and Decomposed Ensemble Modeling for Monthly Runoff Prediction
by Wei Ma, Xiao Zhang, Yu Shen, Jiancang Xie, Ganggang Zuo, Xu Zhang and Tao Jin
Water 2024, 16(21), 3102; https://doi.org/10.3390/w16213102 - 29 Oct 2024
Cited by 2 | Viewed by 1122
Abstract
Monthly runoff prediction is crucial for water resource allocation and flood prevention. Many existing methods use identical deep learning networks to understand monthly runoff patterns, neglecting the importance of predictor selection. To enhance predictive accuracy and reliability, this study proposes an RFECV–SSA–LSTM forecasting [...] Read more.
Monthly runoff prediction is crucial for water resource allocation and flood prevention. Many existing methods use identical deep learning networks to understand monthly runoff patterns, neglecting the importance of predictor selection. To enhance predictive accuracy and reliability, this study proposes an RFECV–SSA–LSTM forecasting approach. It iteratively eliminates predictors derived from SSA decomposition and PACF using recursive feature elimination and cross-validation (RFECV) to identify the most relevant subset for predicting the target flow. LSTM modeling is then used to forecast flows 1–7 months into the future. Furthermore, the RFECV–SSA framework complements any machine-learning-based runoff prediction method. To demonstrate the method’s reliability and effectiveness, its outputs are compared across three scenarios: direct LSTM, MIR–LSTM, and RFECV–LSTM, using monthly runoff historical data from Yangxian and Hanzhong hydrological stations in the Hanjiang River Basin, China. The results show that the RFECV–LSTM method is more robust and efficient than the direct LSTM and MIR–LSTM counterparts, with the smallest number of outliers for NSE, NRMSE, and PPTS under all forecasting scenarios. The MIR–LSTM approach exhibits the worst performance, indicating that single-metric-based feature selection may eliminate valuable information. The SSA time–frequency decomposition is superior, with NSE values remaining stably around 0.95 under all scenarios. The NSE value of the RFECV–SSA–LSTM method is greater than 0.95 under almost all forecasting scenarios, outperforming other benchmark models. Therefore, the RFECV–SSA–LSTM method is effective for forecasting highly nonlinear runoff series, exhibiting high accuracy and generalization ability. Full article
(This article belongs to the Special Issue New Methods and Technologies of Urban Flood Forecasting, Risk Assessment and Response)
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