Flood-based critical sub-watershed mapping: comparative application of multi-criteria decision making methods and hydrological modeling approach

Abstract

The effects of Sub-Watersheds (SWs) on each other can be more important in Flood Generation Potential (FGP). Therefore, the present study aims for prioritizing SWs based on FGP using Multi-Criteria Decision Making (MCDM) Methods including Game Theory (GT), Best-Worst Method (BWM), Analytic Hierarchy Process (AHP), Analytical Network Process (ANP), Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Analytical Network Process (FANP) and comparing its results with Hydrological Modeling Approach (HMA) in the Cheshmeh-Kileh Watershed, Iran. In GT, Condorcet algorithm were used. The best and worst criteria were identi ed using the BWM and compared with other criteria. In AHP and ANP, expert opinions were used and the nal weight of criteria and alternative was calculated using Expert Choice and Super Decision softwares. ArcGIS10.1 software was also used to fuzzy and provide FAHP and FANP. In HMA, HEC-HMS software was used to calculate the discharge with return periods of 10-and 100-year, and nally, in all methods, FGP maps were prepared in three classes and SWs were prioritized. Based on the results of different methods, SWs 9, 2, 7, 10 and 11 were given high FGP priority. Downstream SWs were also in a noncritical state due to dense forest cover and low slope. A comparative evaluation between the methods showed that BWM had the same result as the eld evidence and HMA results and this method provided the best result. Based on SWs prioritization in BWM, high and low FGP were 33.33 and 46.67% of the study area, respectively. After BWM, GT gave a relatively good result. AHP, ANP, FAHP and FANP presented different results, but had poor performance in identifying critical areas. This study showed that optimal MCDM approaches can be used for ood management.

Evaluation of Factors Found to Influence Urban Flood Resilience in China

“…The evaluation of urban flood resilience can be classified as part of the multi-criteria decision-making method (MCDM) [20,26], and MCDMs such as decision-making trial and evaluation laboratory (DEMATEL), data envelopment analysis (DEA), entropy weight method (EM), analytic network process (ANP), and grey relation analysis (GRA) have been widely used in flood studies [26]. Ning et al applied the Fisher information (FI) and data envelopment analysis (DEA) to measure the sustainability of urban systems and also compared the advantages and disadvantages of the two methods [27].…”

A State-of-the-Art Review of the BWM Method and Future Research Agenda

“…■ China is the most prolific country in BWM research, followed by Iran, India, and Turkey. (Wu et al, 2022;Polat et al, 2023), COCOSO (Torkayesh et al, 2021;Ali et al, 2023), MARCOS (Yadav & Kumar, 2023;Ecer et al, 2024), VIKOR (Jain et al, 2023), QFD (Chang et al, 2023), WASPAS (Tavakoli Haji Abadi & Avakh Darestani, 2023), MOORA (Riahi et al, 2023), MULTIMOORA (Koppiahraj et al, 2023), SAW (Darvazeh et al, 2022), TODIM (Kumar et al, 2023), COPRAS (Sahraei et al, 2023), GRA (Hsu et al, 2023), MABAC (Chauhan et al, 2022), DEA (Eskandari et al, 2022), ARAS (Almutairi et al, 2023), and EDAS (Liang et al, 2023), weighting-based MCDM methods, including AHP (Koppiahraj et al, 2023), ANP (Nasiri Khiavi et al, 2023), DEMATEL (Bongo & Seva, 2023), FUCOM (Fazeli & Peng, 2023), and SWARA (Karakuş, 2023), some theories, including game theory (Fard et al, 2022), grey theory (Ulutaş et al, 2022), prospect theory (Zheng et al, 2023), and zero-sum game theory (Li et al, 2021), and mathematical programming, including goal programming (Do et al, 2023), and possibilistic programming (Shaw et al, 2023).…”

Flood‐oriented watershed health and ecological security conceptual modeling using pressure, state, and response (PSR) approach for the Sharghonj Watershed, South Khorasan Province, Iran

“…The development of industry, increase in polluting resources, and human intervention in natural ecosystems to provide welfare and food resources for the ever-increase population of the world caused land degradation (Farzi et al, 2022;Hashem & Marrouch, 2023;Nasiri Khiavi et al, 2023;Sadoddin et al, 2016), changes in vegetation and soil Toma et al, 2023), deforestation (Gompil et al, 2022;Mehdi et al, 2015), climate change (Chamani, Moradi Rekabdar Kolaei et al, 2022;Mohammadlou et al, 2022). The results of such degradations have mirrored in widespread and extensive flood occurrence, land subsidence, and accelerated soil erosion leading to unexpected and sometime uncontrollable natural and socioeconomic disasters (Eslamian & Sabzevari, 2022;Tanoue et al, 2020).…”