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New Methods and Technologies of Urban Flood Forecasting, Risk Assessment...
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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 910

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 (1 paper)

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Research

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
Viewed by 628
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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