Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories

Abstract

Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure, and strongly affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density, PD; livestock density, LD; percentage of farmers and ranchers, PFR; distance to industrial and mining areas, DTIM; distance to tourist and cultural heritage areas, DTTCH; land use; distance to residential areas, DTRe; distance to road, DTR; and distance to stream, DTS) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed and the analytical network process (ANP) was applied.Finally, with aim of overcome ambiguity, reduce uncertainty, and keep the data availability, an appropriate Fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that the variables of land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-FUZZY showed that about 19.2% of the region has a high to very high flood vulnerability.

Flood Exposure and Social Vulnerability Analysis in Rural Areas of Developing Countries: An Empirical Study of Charsadda District, Pakistan

“…As depicted in Figure 2, in the 2010 flooding event, the discharge of Swat River reached 8495 cubic meters per second. Heavy rainfall occurred (27)(28)(29)(30) July 2010) which exceeded the capacity of the river channels, and they were unable accommodate the discharge water. Charsadda District, which is the focal point of this study, is one of the most severely flood-affected districts in the country, with relatively widespread casualties and losses.…”

“…Examples of decision-making approaches are the analytical hierarchy process [24], Delphi [25], and TOPSIS [23,26], among others. However, the major limitation of these methods is determining the interdependency among factors [27].…”

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

“…Problems with sophisticated decision‐making processes such as detecting flood susceptible areas may include many system criteria directly or indirectly interdepended. DEMATEL approach, which was proposed by the Geneva Research Center at Battelle Memorial Institute (Gabus & Fontela, 1973), can efficiently map the direct or indirect relationships and interactions among constituents into a comprehensible structural model of the system under study based on its magnitude and type of criteria (Arabsheibani et al, 2016; Azareh et al, 2019; Chukwuma et al, 2021; Hosseini et al, 2021; Kanani‐Sadat et al, 2019; Safaripour & Rezapour Andabili, 2020; Yazdi et al, 2020). This method separates the elements into two groups named “Cause” and “Effect” and visualizes the structure of the causal relations and interactions among them using graphs and matrices (Falatoonitoosi et al, 2014).…”

“…In recent years, the following methods have been commonly adopted for mapping flood-susceptible areas worldwide: (I) Statistical approaches, including logistic regression and frequency ratio to generate flood susceptibility map (Arabameri, Rezaei, et al, 2019;Samanta et al, 2018;Shafapour Tehrany et al, 2019). (II) Machine learning techniques as a new approach have been applied to create flood susceptibility maps, including Convolutional Neural Network (Khosravi et al, 2020), Bayesianbased machine learning model (Hosseini et al, 2020), Support Vector Machine (Tehrany et al, 2015), random forest (Zhao et al, 2018), Multilayer Perceptron Neural Networks (Popa et al, 2019). (III) Traditional Hydrodynamic and hydraulic models have been adopted in flood susceptibility studies (Gharbi et al, 2016;Pathan & Agnihotri, 2021;Sattar et al, 2020).…”

A Stochastic Conflict Resolution Optimization Model for Flood Management in Detention Basins: Application of Fuzzy Graph Model

“…Floods are substantial natural hazards worldwide due to their destructive impacts on societies and the resulting economic losses [1][2][3][4]; flood management is even more critical for decreasing the disastrous effects [5][6][7][8][9][10] in real-life problems in developing countries such as Turkey [11], China [12,13], and Malaysia [14,15], as well as developed countries such as the USA [16] and Italy [17]. Practical flood management strategies include upstream adjustments [18][19][20], flood defense infrastructure [21][22][23][24], and downstream substructures [25][26][27].…”