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Water
Special Issues
Flood Monitoring, Forecasting and Risk Assessment
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Flood Monitoring, Forecasting and Risk Assessment

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1398

Special Issue Editors

Dr. Monica Rivas Casado

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: surface water flooding; standardised monitoring approaches; systems engineering; disruptive technologies; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paul Leinster

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: environmental policy; environmental regulation; sustainability; governance; monitoring; natural capital; ecosystem services; risk assessment; emergency response; systems-based approaches; operationalizing research findings
Special Issues, Collections and Topics in MDPI journals
Dr. Kriti Mukherjee

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: remote sensing; geoinformatics; data science, mapping, modelling

Special Issue Information

Dear Colleagues,

In recent decades, there has been an increase in the frequency and intensity of flood events across the world. Changes in rainfall patterns driven by underlying climate trends have resulted in a catastrophic loss of human life due to floods. Economic losses caused by floods worldwide have also been of significance. Urban environments are exposed to multiple sources of flooding, including surface water, groundwater, fluvial and coastal waters. The complex interactions between different flood types and the urban landscape require fit-for-purpose management strategies to effectively address the challenges that will affect many countries in the coming years. Monitoring programmes that support accurate input data for forecasting and risk assessment are key to developing and informing such management decisions. This Special Issue focuses on all aspects of flood monitoring, forecasting and risk assessment, from localised rainfall to flood emergency response and recovery. Of particular interest are papers focusing on transferable approaches that can be used at different spatio-temporal scales. This Special Issue aims to cover a full range of challenges within the field, including ensemble probability weather forecasting, river flow verification, climate change driven flood scenarios, and novel monitoring techniques, as well as data science-driven solutions such as digital twins.

Dr. Monica Rivas Casado
Prof. Dr. Paul Leinster
Dr. Kriti Mukherjee
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

  • flood management
  • risk assessment
  • flood types
  • data solutions
  • technological uptake

Published Papers (1 paper)

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Research

24 pages, 11377 KiB  
Article
Flood Detection in Polarimetric SAR Data Using Deformable Convolutional Vision Model
by Haiyang Yu, Ruili Wang, Pengao Li and Ping Zhang
Water 2023, 15(24), 4202; https://doi.org/10.3390/w15244202 - 05 Dec 2023
Viewed by 1106
Abstract
Floods represent a significant natural hazard with the potential to inflict substantial damage on human society. The swift and precise delineation of flood extents is of paramount importance for effectively supporting flood response and disaster relief efforts. In comparison to optical sensors, Synthetic [...] Read more.
Floods represent a significant natural hazard with the potential to inflict substantial damage on human society. The swift and precise delineation of flood extents is of paramount importance for effectively supporting flood response and disaster relief efforts. In comparison to optical sensors, Synthetic Aperture Radar (SAR) sensor data acquisition exhibits superior capabilities, finding extensive application in flood detection research. Nonetheless, current methodologies exhibit limited accuracy in flood boundary detection, leading to elevated instances of both false positives and false negatives, particularly in the detection of smaller-scale features. In this study, we proposed an advanced flood detection method called FWSARNet, which leveraged a deformable convolutional visual model with Sentinel-1 SAR images as its primary data source. This model centered around deformable convolutions as its fundamental operation and took inspiration from the structural merits of the Vision Transformer. Through the introduction of a modest number of supplementary parameters, it significantly extended the effective receptive field, enabling the comprehensive capture of intricate local details and spatial fluctuations within flood boundaries. Moreover, our model employed a multi-level feature map fusion strategy that amalgamated feature information from diverse hierarchical levels. This enhancement substantially augmented the model’s capability to encompass various scales and boost its discriminative power. To validate the effectiveness of the proposed model, experiments were conducted using the ETCI2021 dataset. The results demonstrated that the Intersection over Union (IoU) and mean Intersection over Union (mIoU) metrics for flood detection achieved impressive values of 80.10% and 88.47%, respectively. These results surpassed the performance of state-of-the-art (SOTA) models. Notably, in comparison to the best results documented on the official ETCI2021 dataset competition website, our proposed model in this paper exhibited a remarkable 3.29% improvement in flood prediction IoU. The experimental outcomes underscore the capability of the FWSARNet method outlined in this paper for flood detection using Synthetic Aperture Radar (SAR) data. This method notably enhances the accuracy of flood detection, providing essential technical and data support for real-world flood monitoring, prevention, and response efforts. Full article
(This article belongs to the Special Issue Flood Monitoring, Forecasting and Risk Assessment)
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