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Urban Science
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Flooding Prevention Strategies for Flood-Prone Cities Under Climate Change
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Flooding Prevention Strategies for Flood-Prone Cities Under Climate Change

A special issue of Urban Science (ISSN 2413-8851). This special issue belongs to the section "Urban Environment and Sustainability".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2660

Editors

Dr. Ming Zhong

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510276, China
Interests: enhanced risk and resilience of complex disasters; hydrological remote sensing; digital and intelligent disaster risk prevention and control; infrastructure-based disaster prevention and mitigation
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohong Yang

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: hydrological remote sensing; deep learning–based remote sensing image processing; applications of remote sensing in water resources and environmental monitoring; digital twins and large-scale foundation models
Prof. Dr. Shengmei Yang

E-Mail Website
Guest Editor
Changjiang River Scientific Research Institute, Wuhan 430010, China
Interests: water hazard risk analysis; watershed and urban runoff modeling
Dr. Lu Zhuo

E-Mail Website
Guest Editor
School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK
Interests: remote sensing; physical process-based modelling; machine learning; agent-based modelling; flood; landslide; soil moisture; precipitation; climate change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Song Ye

E-Mail Website
Guest Editor
Changjiang River Scientific Research Institute, Wuhan 430010, China
Interests: spatiotemporal monitoring using remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change is fundamentally reshaping hydrological cycles, leading to more frequent, intense, and unpredictable flooding events that threaten cities globally. Consequently, coastal, riverine, and rapidly urbanizing areas face escalating risks to infrastructure, economies, ecosystems, and human well-being. The increasing complexity of urban flood dynamics calls for a paradigm shift from reactive response to integrated and adaptive prevention strategies for flood-prone cities; therefore, this Special Issue aims to explore innovative methodologies and technologies that enhance flood resilience, and we seek contributions that advance both theoretical frameworks and practical applications in flood risk management in the face of climate uncertainty.

In this Special Issue, original research articles and reviews are welcome, and research areas may include, but are not limited to, the following themes:

(1) Climate-informed flood risk modeling and forecasting, including advances in predictive analytics, ensemble modeling under uncertainty, and the integration of artificial intelligence and remote sensing for early warning systems;

(2) Hybrid grey–green–blue infrastructure systems that combine traditional engineering solutions with ecological design to enhance adaptive capacity and multifunctional benefits;

(3) Nature-based solutions such as constructed wetlands, urban green corridors, and permeable landscapes that mitigate flood impacts;

(4) Infrastructure resilience focusing on the performance evaluation, retrofitting strategies, and lifecycle management of critical systems under extreme events;

(5) Urban planning and land use adaptation, including climate-responsive zoning, spatial optimization for risk reduction, and community-based resilience planning.

Dr. Ming Zhong
Dr. Xiaohong Yang
Prof. Dr. Shengmei Yang
Dr. Lu Zhuo
Prof. Dr. Song Ye
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access monthly 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 1800 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

  • flooding
  • risk
  • urban infrastructure
  • resilience
  • climate change
  • remote sensing

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Published Papers (3 papers)

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Research

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20 pages, 1231 KB  
Article
Text-Prompt-Based AI-Generated Virtual Image Augmentation for Data-Scarce Flood Detection in Urban Flood-Prone Areas
by Hanseon Joo and Ook Lee
Urban Sci. 2026, 10(7), 398; https://doi.org/10.3390/urbansci10070398 - 10 Jul 2026
Viewed by 483
Abstract
Urban flood detection requires visual examples of flooded streets and alleys, but such event-state images are difficult to collect at scale. This study examines whether sparse real-image training sets can be strengthened using text-prompt-only AI-generated virtual imagery for ground-level flood detection in flood-prone [...] Read more.
Urban flood detection requires visual examples of flooded streets and alleys, but such event-state images are difficult to collect at scale. This study examines whether sparse real-image training sets can be strengthened using text-prompt-only AI-generated virtual imagery for ground-level flood detection in flood-prone urban areas. Building on AlleyFloodNet, the generated images were used only as condition-specific training augmentation, while validation and testing were conducted exclusively on real images. Across eight ImageNet-pretrained architectures and three random seeds, the results show that virtual imagery is not a substitute for real flood observations. When virtual images dominated the training set, performance declined. However, when a sufficient real-image anchor was available, virtual augmentation improved the highest observed fixed-test performance. The strongest mixed-condition result was obtained by EfficientNet-B2 under Real30_Aug70, reaching 91.00% accuracy, 89.48% flooded-class recall, and 90.79% macro-F1. These findings suggest that prompt-only virtual imagery can help mitigate real-image scarcity in selected training conditions, but its benefit depends on the real-to-virtual composition and model architecture. Full article
(This article belongs to the Special Issue Flooding Prevention Strategies for Flood-Prone Cities Under Climate Change)
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22 pages, 4100 KB  
Article
Explainable Machine Learning-Based Urban Waterlogging Prediction Framework
by Yinghua Deng and Xin Lu
Urban Sci. 2026, 10(3), 156; https://doi.org/10.3390/urbansci10030156 - 13 Mar 2026
Cited by 2 | Viewed by 1273
Abstract
Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional [...] Read more.
Urban waterlogging has become a critical challenge to urban sustainability under the combined pressures of rapid urbanization and increasingly frequent extreme weather events. However, traditional predictive models struggle to achieve real-time, point-specific early warning effectively, primarily due to the interference of redundant high-dimensional data and the inability to handle severe data imbalance. This study proposes a lightweight and interpretable machine learning framework for real-time waterlogging hotspot prediction, based on a multi-dimensional feature space. Specifically, we implement a Lasso-based mechanism to distill 37 multi-source variables into five core determinants. This process effectively isolates dominant environmental drivers while filtering noise. To further overcome the recall bottleneck, we propose a Synthetic Minority Over-sampling Technique based on Weighted Distance and Cleaning (SMOTE-WDC) algorithm that incorporates weighted feature distances and density-based noise cleaning. Validating the framework on datasets from Shenzhen (2023–2024), we demonstrate that the integrated Gradient Boosting Decision Tree (GBDT) model integrated with this strategy achieves optimal performance using only five features, yielding an F1-score of 0.808 and an Area Under the Precision-Recall Curve (AUC-PR) of 0.895. Notably, a Recall of 0.882 is attained, representing a 4.6% improvement over the baseline. This study contributes a cost-effective, high-sensitivity approach to disaster risk reduction, advancing predictive urban waterlogging management. Full article
(This article belongs to the Special Issue Flooding Prevention Strategies for Flood-Prone Cities Under Climate Change)
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Review

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30 pages, 7414 KB  
Review
Integrating Technology into Sustainable Urban Planning: Assessing Urban Flood Vulnerability and Strengthening Community Preparedness for Enhanced Water Security
by Ahyahudin Sodri, Mauliza Fatwa Yusdian, Haruki Agustina, Nuraeni Nuraeni and Riska Nur Azizah
Urban Sci. 2026, 10(7), 404; https://doi.org/10.3390/urbansci10070404 - 12 Jul 2026
Viewed by 456
Abstract
The increasing frequency of climate change and extreme weather makes the issue of urban flood vulnerability an important one. Flood disaster mitigation efforts have progressed rapidly with the utilization of technology in flood risk assessment and community preparedness. However, there is still a [...] Read more.
The increasing frequency of climate change and extreme weather makes the issue of urban flood vulnerability an important one. Flood disaster mitigation efforts have progressed rapidly with the utilization of technology in flood risk assessment and community preparedness. However, there is still a gap between technology and community engagement in preparedness. This study was conducted to analyze the trend of technology-based urban flood vulnerability and community preparedness integration. The study used bibliometric methods to see research trends and a systematic review of Scopus data with Biblioshiny, VOSviewer, and Convidence to identify relevant research articles. Trends in urban flood vulnerability show an increasing use of advanced technologies such as multivariate LSTM (Long Short-Term Memory) artificial neural networks, LiDAR (Light Detection and Ranging), GIS (Geographic Information System), and blockchain for flood risk assessment. There are gaps in community engagement and preparedness, highlighting the need for a comprehensive approach that combines technology with community-based strategies. A balance between technology implementation and community engagement is needed to improve community preparedness and safeguard water security in flood-prone urban areas. The development of an urban flood vulnerability index can bridge the technology gap with standardized community engagement. All the articles in the study contribute greatly to the changes and improvements in flood disaster mitigation, as well as to the role of technology and preparedness. The gap between technology and community engagement in preparedness requires an urban flood vulnerability index with comprehensive strategies. Full article
(This article belongs to the Special Issue Flooding Prevention Strategies for Flood-Prone Cities Under Climate Change)
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