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Flash-Flood Susceptibility, Forecast and Warning

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 14340

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Special Issue Editor

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Dear Colleagues,

Flash-floods, due to their unexpected nature, can cause severe economic damages and loss of human life. They may be caused either by extreme precipitation, by the failure of human-made structures, such as dams, or by complex water–snow interactions. Flash-flood early warning systems represent the most effective non-structural measure, which should be taken in order to mitigate the negative effects of these hydrological hazards. An efficient and operational flash-flood early warning system should be based on the following two major components: a map representing a very accuracte detection of flash-flood susceptible areas and a performant meteorological radar network, which are able detect the cloud systems that are likely to generate heavy rainfalls over the susceptible areas, and also to accurately estimate the rainfall amount and their accumulation.

In the light of the above-mentioned aspects, the main scope of this Special Issue is to collect the state of the art findings related to the application of newest techniques and software in order to estimate flash-flood susceptibity and the likelihood of heavy rainfall occurrence, and also the studies related to the development of new flash-flood early warning system. High-quality original research papers that present theoretical frameworks, methodologies, and applications of case studies from a single- or cross-country perspectives are welcome, as well as review articles.

Dr. Romulus Costache
Guest Editor

Manuscript Submission Information

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Keywords

flash-flood forecast systems
flash-flood susceptiblity
flash-flood warnings encoding
machine learning
geographic information systems
remote sesning
heavy rainfall
meteorological radar

Published Papers (2 papers)

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Research

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27 pages, 6797 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

by Asish Saha, Subodh Chandra Pal, Alireza Arabameri, Thomas Blaschke, Somayeh Panahi, Indrajit Chowdhuri, Rabin Chakrabortty, Romulus Costache and Aman Arora

Water 2021, 13(2), 241; https://0-doi-org.brum.beds.ac.uk/10.3390/w13020241 - 19 Jan 2021

Cited by 99 | Viewed by 5795

Abstract

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871). Full article

(This article belongs to the Special Issue Flash-Flood Susceptibility, Forecast and Warning)

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Review

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13 pages, 2030 KiB

Open AccessFeature PaperEditor’s ChoiceReview

An Overview of Flood Risk Analysis Methods

by Daniel Constantin Diaconu, Romulus Costache and Mihnea Cristian Popa

Water 2021, 13(4), 474; https://0-doi-org.brum.beds.ac.uk/10.3390/w13040474 - 11 Feb 2021

Cited by 17 | Viewed by 7467

Abstract

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies. Full article

(This article belongs to the Special Issue Flash-Flood Susceptibility, Forecast and Warning)

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