Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm

Lin, Kairong; Chen, Haiyan; Xu, Chong‐Yu; Yan, Ping; Lan, Tian; Liu, Zhiyong; Dong, Chunyu

doi:10.1016/j.jhydrol.2020.124696

Cited by 118 publications

(53 citation statements)

References 57 publications

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“…The analytic hierarchy process (AHP) is a powerful multi-criteria method introduced by Thomas Saaty, that helps to make decisions in the face of complex problems that have multiple conflicting and subjective criteria (Saaty, 1980(Saaty, , 1986(Saaty, , 1990. The AHP has been used as a tool for decision making in different fields of science, such as health (Requia et al, 2020), natural science (Lin et al, 2020) and engineering (Yin Zhang et al, 2020;Zhou & Yang, 2020). AHP begins by defining the problem to solve (the objective) and decomposing the problem into a hierarchy of decisions (Vassou et al, 2006), then a paired comparison is used between the criteria with respect to the objective, as well as among the alternatives with respect to each criterion, with a view to establishing priorities among the elements of the hierarchy.…”

Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

Badillo-Rivera

Esteves

oacutepez

et al. 2020

Preprint

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The novel coronavirus disease (COVID-19) generated by the SARS-CoV-2 virus was originated in the city of Wuhan (China) in December 2019, the virus began to spread in other regions of China until it spread to the rest of the world. In this research, an analysis was made of environmental factors (tropospheric column of NO2, vertical airflow, percentage of solid waste disposed of in open dumps and percentage of the population without any mechanism of faeces disposal) and social factors (levels of monetary poverty, percentage of the number of hospitals per population and vulnerable population) that could directly and indirectly affect the spread of SARS-CoV-2 virus in the regions of Peru. Remote sensing techniques, geographic information systems and an analysis under the multi-parametric statistical approach proposed by Saaty were used to determine which regions present greater susceptibility, vulnerability and risk of spreading the SARS-CoV-2 virus. The results show that the prevalence of high values of tropospheric NO2 and values close to 0 Pa/s of the vertical airflow were directly related to the number of positive cases by COVID-19. In addition, it was found that 68% of the regions of Peru are at a "high" and "very high" risk of spreading SARS-CoV-2 virus, and most of them are in northern and central Peru (Callao, Tumbes, Piura, Loreto, Lambayeque, Huancavelica, Amazonas, Cajamarca, Ucayali, Huanuco and among others), therefore, special care should be taken with the measures adopted after social isolation in order to avoid the resurgence and collapse of the of health systems. It concludes that public policies on air quality management, integrated solid waste management and sanitation services should be improved in order to reduce the risk of spreading the SARS-CoV-2 virus. This research can be replicated on a longer scale, including more variables.

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Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

Badillo-Rivera

Esteves

oacutepez

et al. 2020

Preprint

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“…The results of prioritizing the effective factors in flooding using the AHP method and the relationship between flood occurrences and each class of effective factors using WOE model are presented in Table 3. As indicated, all of the quantitative and qualitative factors were classified into 5 classes based on natural breaks in the ArcGIS environment (Lin et al 2020;Ogato et al 2020). In the next columns, the number of flood pixels (training floods) and total pixels for each class were specified.…”

Section: Flood Susceptibility Mappingmentioning

confidence: 99%

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling 

Tabarestani

Afzalimehr

2021

Preprint

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Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

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“…The results of W-C showed that surface water quality was the most important indicator with a weight of 0.3784, followed by runoff control indicator with a weight of 0.3441. This result could be explained by the fact that urban water management, integrating water pollution and runoff floods, is the priority target of SC construction (Li et al 2019, Lin et al 2020,…”

Section: The Construction Of Priority Index Modelmentioning

confidence: 99%

“…The areas surrounding the center of the watershed are newly built-up areas, which is the best time for SC integration, supporting the highest priority for LID construction in these areas. The center of the watershed (core areas and downtown areas) is still a high density of residential and commercial areas accompanied by a high risk of casualties and property losses when facing disasters (Lin et al 2020), requiring a higher priority of SC construction. So, the spatial distribution of priority sites, shown in Fig.…”

Section: Verificationmentioning

confidence: 99%

Prioritizing Sponge City Sites in Rapidly Urbanizing Watersheds Using Multi-Criteria Decision Model

Zhang

Chen

Zhang

et al. 2021

Preprint

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Spatial planning is crucial for Sponge City (SC) construction; however, prioritizing SC sites at the watershed scale has not been fully explored. In this study, a multi-criteria decision model, considering demand and suitability of SC construction, was established by monitoring, model simulation and index calculation. This new model was then tested in a rapidly urbanizing watershed, Beijing, China and the priority of SC construction at both grid scale (1km 1km) and subwatershed scale was ranked. The results showed that the highest priority was found in emerging regions where urbanization is ongoing, and followed by urban core areas. In addition, six indexes were identified by clustering heatmaps as key factors affecting the priority of SC planning, including topographic index, water pollution index, pollution rate based on the state standard of surface water environment quality, urbanization planning, urban levels, and vegetation index, which could guide SC planning in data-lacking regions. The approach and findings in this study can not only provide helpful references for watershed managers and urban planners but also can be easily used in other regions.

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Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm

Cited by 118 publications

References 57 publications

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling

Prioritizing Sponge City Sites in Rapidly Urbanizing Watersheds Using Multi-Criteria Decision Model

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Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm

Cited by 118 publications

References 57 publications

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

Environmental and social analysis as risk factors for the spread of the novel coronavirus (SARS-CoV-2) using remote sensing, GIS and analytical hierarchy process (AHP): Case of Peru

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling&nbsp;

Prioritizing Sponge City Sites in Rapidly Urbanizing Watersheds Using Multi-Criteria Decision Model

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A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling