Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas

Dehghanian, Naser; Nadoushani, S. Saeid Mousavi; Saghafian, Bahram; Akhtari, Rouhangiz

doi:10.1007/s11269-019-02385-7

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The results indicate that prioritised flood source areas are sensitive to the spatial distribution of more frequent rainfall events, rather than rainfall events that have high return periods. Dehghanian et al (2019) compared the UFR approach with self-organising feature maps and fuzzy c-means (SOMFCM) algorithms as a method for applying FSA identification; however, it is difficult to make a direct comparison between the two approaches, since SOMFCM cannot provide absolute values for FSA and hence cannot be represented on a map. Roughani et al (2007) applied isochrones for spatial analysis and sub-catchment grouping.…”

Section: Flood Index Categorisaɵonmentioning

confidence: 99%

A review of modelling methodologies for flood source area (FSA) identification

et al. 2021

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Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises.

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Section: Flood Index Categorisaɵonmentioning

confidence: 99%

A review of modelling methodologies for flood source area (FSA) identification

et al. 2021

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“…One of the most important tasks in flood control is identifying the best methods for locating the primary sources of flooding across a catchment, in order to improve flood prevention techniques that is also known as flood source areas (FSA) (Singh et al, 2021), through accurate simulation and analysis of runoff generation processes at the sub-basin scale. In an attempt to solve this problem, many systematic methods such as hydrological models (Abdulkareem et al, 2018;Dehghanian et al, 2019;Maghsood et al, 2019), GIS-based methods (Cabrera & Lee, 2019;Hong & Abdelkareem, 2022;Mukherjee & Singh, 2020;Osei et al, 2021), remote sensing methods (Sadiq et al, 2022;Sharma et al, 2019;Syifa et al, 2019), multi-criteria decision methods (Ajjur & Mogheir, 2020;Hadian et al, 2022;Pham et al, 2021;Roy et al, 2021), and machine learning and data mining methods (Ha & Kang, 2022;Luu et al, 2021;Rahman et al, 2021) have been used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of hydrological modeling, Artificial Neural Networks and multi-criteria decision making approaches for determining Flood Source Areas

Azari,

Mahmoodi,

Dastorani

et al. 2024

Preprint

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Flood risk management is a critical task which necessitates flood forecasting and identifying flood source areas for implementation of prevention measures in a basin. Hydrological models, multi-criteria decision models (MCDM) and data-driven models such as Artificial Neural Networks (ANN) have been used for identifying flood source areas within a watershed. The aim of this study is comparing the results of hydrological modeling, MCDM and ANN approaches in order to identify and prioritize flood source areas. The study results show that the classification results of the hydrological model and the artificial neural network have a significant correlation; also the correlation between the TOPSIS method with the hydrological model (0.252) and the artificial neural network (0.233) indicates that none of the sub-basins in the Very high, High and Very low classes are similar in the above methods. Since the neural network model has simulated the HEC-HMS classifications very accurately, it can be concluded that this model has performed very well as compared to the TOPSIS multi-criteria decision-making method.

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Random Walks Partitioning and Network Reliability Assessing in Water Distribution System

Tan

et al. 2021

Water Resour Manage

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Performance Evaluation of a Fuzzy Hybrid Clustering Technique to Identify Flood Source Areas

Cited by 9 publications

References 30 publications

A review of modelling methodologies for flood source area (FSA) identification

A review of modelling methodologies for flood source area (FSA) identification

Comparison of hydrological modeling, Artificial Neural Networks and multi-criteria decision making approaches for determining Flood Source Areas

Random Walks Partitioning and Network Reliability Assessing in Water Distribution System

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