Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods

Akay, Hüseyın; Koçyiğit, Müsteyde Baduna

doi:10.1007/s00500-020-04792-0

Cited by 29 publications

(11 citation statements)

References 49 publications

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“…In addition to the comparison of the ranking orders, the Spearman's rank correlation coefficient (

ρ

) is introduced to evaluate the ranking lists generated from different approaches, which is defined as 61 :

ρ = 1 - \frac{6 \sum_{i = 1}^{n} d_{i}^{2}}{n (n^{2} - 1)},

where

n

is the number of ranks and

d_{i}

is the distance between the ranks at position

i

. If

ρ \geq 0.8

, it denotes a very strong correlation between the two compared ranking lists 62 …”

Section: Discussionmentioning

confidence: 99%

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A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

Zhu

2021

Int J Intell Syst

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Smart product‐service systems (Smart PSS) are receiving increasing attention due to their potentials to satisfy customer needs and create sustainability to meet hybrid concerns. As a value co‐creation business paradigm, various sustainable value propositions (SVPs) and their assessments come from stakeholders' subjective judgments, which values are imprecise, vague, and even inconsistent. The reliability of these values also varies with the confidence and cognitive bias of the respective stakeholder. How to evaluate these co‐creative SVPs to ensure an objective and reliable result under such uncertain environments becomes a critical issue. To fill this gap, this paper proposes a rough‐Z‐number extended multi‐criteria group decision‐making framework integrating the rough‐Z‐number, DEMATEL (decision making trial and evaluation laboratory), and group decision‐making strategy to evaluate the co‐creative SVPs for Smart PSS. A novel rough‐Z‐number is presented to deal with the uncertainty and reliability representation of the stakeholder's individual assessment as well as the aggregation and subjectivity manipulation of these risk assessments from different stakeholders. A rough‐Z‐number extended DEMATEL is then proposed to determine the risk prioritization of the co‐creative SVPs. Experimental results and comparative studies demonstrate the superiority of the proposed rough‐Z‐number‐based DEMATEL in handling the uncertainty and reliability characterization as well as the subjectivity manipulation during the evaluation of the co‐creative SVPs.

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“…In addition to the comparison of the ranking orders, the Spearman's rank correlation coefficient (

ρ

) is introduced to evaluate the ranking lists generated from different approaches, which is defined as 61 :

ρ = 1 - \frac{6 \sum_{i = 1}^{n} d_{i}^{2}}{n (n^{2} - 1)},

where

n

is the number of ranks and

d_{i}

is the distance between the ranks at position

i

. If

ρ \geq 0.8

, it denotes a very strong correlation between the two compared ranking lists 62 …”

Section: Discussionmentioning

confidence: 99%

“…In addition to the comparison of the ranking orders, the Spearman's rank correlation coefficient (ρ) is introduced to evaluate the ranking lists generated from different approaches, which is defined as 61 :…”

Section: Discussionmentioning

confidence: 99%

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

Zhu

2021

Int J Intell Syst

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show abstract

“…Where wj: weight criterion j; and yij: matrix element 3. Create a positive ideal solution matrix and a negative ideal solution matrix which can be seen in equations ( 9) to (10).…”

Section: Determining the Leading Industry Using The Technique For Order Preference Methods By Similarity To Ideal Solution (Topsis)mentioning

confidence: 99%

“…Several decision-making methods include AHP, ANP, VIKOR, TOPSIS, VIKOR, SAW, ELECTRE, PROMOTEE, MAUT, and MPE [10]. The decision-making method used in this study is the MCDM method by integrating the Analytical Hierarchy Process (AHP) to calculate the weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the order of priority.…”

Section: Introductionmentioning

confidence: 99%

An integrated AHP-TOPSIS framework for determination of leading industrial sectors

Rahmayanti

Meuthia

Albin

et al. 2021

j. sist. manaj. ind.

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This study aims to determine the leading industry in Padang Pariaman Regency, West Sumatera, Indonesia, based on data from the Central Statistics Agency and expert opinion on the Regency Industrial Development Plan. This research combines qualitative and quantitative techniques. This study uses four experts' opinions consisting of three governments and one academician. The criteria and sub-criteria are determined based on the locally adapted National Industrial Development Master Plan. The method used in this study is a combination of the Multi-Criteria Decision Making (MCDM) method, which integrates the Analytical Hierarchy Process (AHP) to calculate the weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the order of priority. The top five leading processing industries were selected: the food industry, the leather/footwear industry, the chemical industry, the apparel industry, and other processing industries.

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“…In scale of catchment, given the inherent complexity of formulating flood risk management strategies and its high uncertainty due to some reasons such as large input data and long processing time, it is necessity to select sub-watershedsas a small-scale hydrological unit to prioritize them based on their flood potential (Aher et al, 2014;Anees et al, 2019;Shivhare et al, 2018). In this context, there are variety of approaches available to analysis and prioritize sub-watersheds using Multi Criteria Decision Analysis (MCDA) (Akay and Koçyiğit, 2020;Chitsaz and Banihabib, 2015;Ghaleno et al, 2020;Sepehri et al, 2019c), Soil and Water Assessment Tool (SWAT) (Mishra et al, 2007;Talebi et al, 2019a), artificial neural network (ANN) (Dehghanian et al, 2020), Storm Water Management Model (SWMM) (Babaei et al, 2018), support vector machine (SVM) (Fan et al, 2018;Tehrany et al, 2014) and The Hydrologic Modeling System (HEC-HMS) (Malekinezhad et al, 2017;Talebi et al, 2019b). Among aforementioned methods, MCDA has been taking into account due to its capability to handle nonlinear and complex problems and its usability to prioritize ungauged watershed.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

Meshram

Ildoromi

Sepehri

2021

Preprint

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Flood is one of the major problems of the sad ekbatan watershed, northern of Hamadan province, Iran. This problem imposes high damages to the economic issue. Therefore, prioritization of the study area based on the flooding degree can be considered for identifying hot spot flooded areas for performing soil and water conservation practices. In this study, in order to prioritize sub-watersheds of the case study from viewpoint of flooding degree, five flood-related criteria i.e. entropy of drainage network (En), index of connectivity (IC), stream power index (SPI), curvature (C) and curve number (CN) were considered, then fuzzy based Best Worse Multi Criteria Decision Making (F-BWM) Method was used to assigning weights to used criteria and combination them to achieve flooding degree for each sub-watershed. The results of prioritization of sub-watersheds indicated that the sub-watersheds 14 and 21 are most and least susceptibility areas to flooding correspondingly.

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Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods

Cited by 29 publications

References 49 publications

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

An integrated AHP-TOPSIS framework for determination of leading industrial sectors

Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

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