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Smart Flood Resilience Integrating AI and Hydraulic and Horologic Modeling

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 3630

Special Issue Editors

Advanced Analytics, MAPFRE Insurance, Webster, MA 01510, USA
Interests: urban resilience; data science; resilient infrastructure; network systems; data analytics

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Guest Editor
M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32611-5701, USA
Interests: intelligent computing applied to civil and construction engineering and management; artificial intelligence for modeling AEC systems

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Guest Editor
Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Interests: drones and sensors; 2D image processing; 3D point cloud processing; infrared data analysis; high-performance computing; cloud computing; data modeling; image-based diagnostics

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Guest Editor
Department of Construction Management, Guangzhou University, Guangzhou 510006, China
Interests: urban resilience; data science; network science; AI in construction management

Special Issue Information

Dear Colleagues,

Recent urban floods, such as the 2021 Henan floods and Hurricane Harvey in 2018, have tremendously impacted society by causing breakdowns of supply chains, failure of civil infrastructures, and damage to homeowner properties. Around the world, various cities have already demonstrated how a proactive, coordinated response to flood events yields immediate results in terms of recovering from shocks and lays the foundations for long-term resilience. Smart resilience intersects with urban resilience, AI, and big data techniques. Urban resilience denotes the ability of an urban system to maintain the functionality needed for people to reduce social, economic, physical, and well-being impacts due to floods and hurricanes. Traditional hydraulic and horologic models are widely used for flood risk mapping and impact assessment with hydraulic features, and topographic and precipitation data. Meanwhile, advanced sensors and monitoring devices have generated large volumes of community-scale big data, such as human mobility, social network communications, transactions, insurance claims, and traffic data, which provide more opportunities to investigate urban smart resilience and strategies to increase it through AI and machine learning technologies. Therefore, smart flood resilience in this context will integrate AI and traditional hydrologic/hydraulic modelling approaches for enhanced flood resilience. Given that, this Special Issue seeks submissions under the theme of smart flood resilience.

Potential topics include, but are not limited to:

  • Urban AI and big data analytics for situational awareness and crisis management in floods/hurricanes;
  • Network systems modeling and analysis for social and infrastructure networks;
  • Flood risk modelling and impact assessment integrating hydrologic/hydraulic modeling and AI approaches;
  • Flood risk management, mitigation, and adaptation;
  • Spatiotemporal analysis of social and built environmental resilience;
  • Equity issues from smart flood resilience (e.g., identification of vulnerable regions, access to critical facilities, and guidelines for homeowner insurance);
  • Smart and sustainable urban food systems in the context of floods and hurricanes.

Dr. Faxi Yuan
Prof. Dr. Ian Flood
Dr. Kaiwen Chen
Dr. Yuanxin Zhang
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. Sustainability 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 2400 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

  • smart resilience
  • urban resilience
  • urban AI
  • hydrologic/hydraulic modelling
  • resilient infrastructure
  • network systems

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Published Papers (2 papers)

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Research

20 pages, 5880 KiB  
Article
Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach
by Gabriele Medio, Giada Varra, Çağrı Alperen İnan, Luca Cozzolino and Renata Della Morte
Sustainability 2024, 16(12), 5246; https://doi.org/10.3390/su16125246 - 20 Jun 2024
Viewed by 1258
Abstract
Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of water resources and sinkhole events. Sinkholes are often caused by internal erosion and fluidization of the soil surrounding leaking pipes, [...] Read more.
Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of water resources and sinkhole events. Sinkholes are often caused by internal erosion and fluidization of the soil surrounding leaking pipes, with the formation of soil cavities that may eventually collapse. This in turn causes road disruption and building foundation damage, with possible victims. While the loss of precious water resources is a well-known problem, less attention has been paid to anthropogenic sinkhole events generated by leakages in water distribution systems. With a view to improving urban smart resilience and sustainability of urban areas, this study introduces an innovative framework to localize leakages based on a Machine learning model (for the training and evaluation of candidate sets of pressure sensors) and a Genetic algorithm (for the optimal sensor set positioning) with the goal of detecting and mitigating potential hydrogeological urban disruption due to water leakage in the most sensitive/critical locations. The application of the methodology on a synthetic case study from literature and a real-world case scenario shows that the methodology also contributes to reducing the depletion of water resources. Full article
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23 pages, 6517 KiB  
Article
Numerical Investigation of Scour Downstream of Diversion Barrage for Different Stilling Basins at Flood Discharge
by Muhammad Waqas Zaffar, Ishtiaq Hassan, Umair Latif, Shah Jahan and Zeeshan Ullah
Sustainability 2023, 15(14), 11032; https://doi.org/10.3390/su151411032 - 14 Jul 2023
Cited by 3 | Viewed by 1593
Abstract
The hydraulic performance of stilling basins is affected by their size and geometry, which can be predicted by local scour. In 2008, based on a rigid bed study, the stilling basin of Taunsa barrage was remodeled, in which the old friction and baffle [...] Read more.
The hydraulic performance of stilling basins is affected by their size and geometry, which can be predicted by local scour. In 2008, based on a rigid bed study, the stilling basin of Taunsa barrage was remodeled, in which the old friction and baffle blocks were replaced with chute blocks and end sills. However, the study did not consider the effects of the remodeled basin on the erodible bed and only investigated hydraulic jumps. Therefore, this study developed FLOW-3D scour models for a designed flow of 24.28 m3/s/m to investigate the flow field and local scouring downstream of old and remodeled basins. The results showed that as compared to Large Eddy Simulation (LES) and Standard K-ε models, the Renormalization Group (RNG-K-ε) model predicted the scour profiles with better accuracy, for which the coefficient of determination (R2) reached 0.736, 0.823, and 0.747 for bays 33, 34, and 55, respectively. Downstream of the remodeled basin, the net change in sediment bed was 88%, 91%, and 95% in the LES, Standard, and RNG-K-ε models, respectively. However, downstream of the old basin, the net change in sediment bed reached 51%. Conclusively, based on the results, the study suggests investigating scour downstream of Taunsa Barrage using other discharges and sediment transport rate equations. Full article
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