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Water
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Innovative Approaches Applied to Flood Risk Management in Urban Areas
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Innovative Approaches Applied to Flood Risk Management in Urban Areas

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 6289

Special Issue Editors

Dr. Susana Pereira

E-Mail Website
Guest Editor
Centre of Geographical Studies, IGOT, University of Lisbon, 1649-004 Lisboa, Portugal
Interests: natural hazards and risk assessment; hydro-geomorphological risks; slope instability; Geographical Information Systems; land use planning
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Pinto Santos

E-Mail Website
Guest Editor
Centre of Geographical Studies, Institute of Geography and Spatial Planning, LA TERRA, University of Lisbon, Lisbon, Portugal
Interests: hydrology; flood hazard; risk assessment; disaster databases; vulnerability studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays stakeholders have begun to face the challenge of preparing flood risk management (FRM) strategies to reduce risk and losses (eg. risk prevention, defense, mitigation, preparation, and recovery). FRM needs to be supported by comprehensive knowledge about the driving forces that define the flood risk and take into consideration the scale where FRM-related decisions are made, specially in the urban areas.

Understanding the drivers of flood risk at the local and urban areas can inform policy makers and planners about the types of measures that should be prioritized for reduction, adaptation, and mitigation in FRM. By also addressing exposure and vulnerability, a greater insight on the causes and potential solutions, in order to reduce flood risk, can be achieved.

Flood risk management plans can be based on three main strategies: (i) measures of a structural nature (defense works, river channeling, etc.); (ii) soft measures through cost-benefit analysis and similar approaches (e.g. risk prevention, education, risk, preparedness, or spatial planning) as well as of the blue and green infrastructure in reducing disaster risk; and (iii) non-structural measures (mostly plans, early warning systems, and civil protection exercises).

This Special Issue  aims to include contributions with case studies or research articles related to flood risk assessment and risk management strategies (structural, soft and non-structural measures), in urban areas worldwide, addressing both spatial and temporal changes in flood risk drivers, i.e. hazard, exposure, and vulnerability. Additionally, are welcome studies about the impact of flood risk management measures in the hydrological models, and in the interventions at the basin scale in urban areas and adaptation strategies to climate change.

Dr. Susana Pereira
Dr. Pedro Pinto Santos
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

  • floods
  • risk management
  • risk analysis
  • urban areas
  • flood modelling

Published Papers (2 papers)

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Research

20 pages, 3251 KiB  
Article
The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models
by Yue Zhang, Zhaohui Gu, Jesse Van Griensven Thé, Simon X. Yang and Bahram Gharabaghi
Water 2022, 14(11), 1794; https://doi.org/10.3390/w14111794 - 02 Jun 2022
Cited by 15 | Viewed by 2608
Abstract
An early warning flood forecasting system that uses machine-learning models can be utilized for saving lives from floods, which are now exacerbated due to climate change. Flood forecasting is carried out by determining the river discharge and water level using hydrologic models at [...] Read more.
An early warning flood forecasting system that uses machine-learning models can be utilized for saving lives from floods, which are now exacerbated due to climate change. Flood forecasting is carried out by determining the river discharge and water level using hydrologic models at the target sites. If the water level and discharge are forecasted to reach dangerous levels, the flood forecasting system sends warning messages to residents in flood-prone areas. In the past, hybrid Long Short-Term Memory (LSTM) models have been successfully used for the time series forecasting. However, the prediction errors grow exponentially with the forecasting period, making the forecast unreliable as an early warning tool with enough lead time. Therefore, this research aimed to improve the accuracy of flood forecasting models by employing real-time monitoring network datasets and establishing temporal and spatial links between adjacent monitoring stations. We evaluated the performance of the LSTM, the Convolutional Neural Networks LSTM (CNN-LSTM), the Convolutional LSTM (ConvLSTM), and the Spatio-Temporal Attention LSTM (STA-LSTM) models for flood forecasting. The dataset, employed for validation, includes hourly discharge records, from 2012 to 2017, on six stations of the Humber River in the City of Toronto, Canada. Experiments included forecasting for both 6 and 12 h ahead, using discharge data as input for the past 24 h. The STA-LSTM model’s performance was superior to the CNN-LSTM, the ConvLSTM, and the basic LSTM models when the forecast time was longer than 6 h. Full article
(This article belongs to the Special Issue Innovative Approaches Applied to Flood Risk Management in Urban Areas)
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16 pages, 1599 KiB  
Article
Identification of Rainfall Thresholds Likely to Trigger Flood Damages across a Mediterranean Region, Based on Insurance Data and Rainfall Observations
by Katerina Papagiannaki, Vassiliki Kotroni, Kostas Lagouvardos, Antonis Bezes, Vasileios Vafeiadis, Ioanna Messini, Efstathios Kroustallis and Ioannis Totos
Water 2022, 14(6), 994; https://doi.org/10.3390/w14060994 - 21 Mar 2022
Cited by 2 | Viewed by 3242
Abstract
Flood-producing rainfall amounts have a significant cumulative economic impact. Despite the advance in flood risk mitigation measures, the cost of rehabilitation and compensation of citizens by the state and insurance companies is increasing worldwide. A continuing challenge is the flood risk assessment based [...] Read more.
Flood-producing rainfall amounts have a significant cumulative economic impact. Despite the advance in flood risk mitigation measures, the cost of rehabilitation and compensation of citizens by the state and insurance companies is increasing worldwide. A continuing challenge is the flood risk assessment based on reliable hazard and impact measures. The present study addresses this challenge by identifying rainfall thresholds likely to trigger economic losses due to flood damages to properties across the Athens Metropolitan Area of Greece. The analysis uses eight-year rainfall observations from 66 meteorological stations and high spatial resolution insurance claims on the postal code segmentation. Threshold selection techniques were applied based on the ROC curves widely used to assess the performance of binary response models. The model evaluates the probability of flood damages in terms of insurance claims in this case. Thresholds of 24-h rainfall were identified at the municipal level, as municipalities are the first administration level where decision making to address the local risks for the citizens is needed. The rainfall thresholds were further classified to estimate and map the local risk of flood damages. Practical implications regarding the applicability of the detected thresholds in early-warning systems are also discussed. Full article
(This article belongs to the Special Issue Innovative Approaches Applied to Flood Risk Management in Urban Areas)
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