Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness

Hadian, Sanaz; Afzalimehr, Hossein; Soltani, Negar; Tabarestani, Ehsan Shahiri; Pham, Quoc Bao

doi:10.1080/10106049.2022.2107714

Cited by 8 publications

(3 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mitra et al (2022) claimed the TOPSIS model is highly recommended for flood susceptibility analysis in the sub-Himalayan region based on MAE, MSE, and RMSE. Also, Pathan et al (2022) and Hadian et al (2022) revealed that the TOPSIS approach estimates more precise flood risk coverage than the other MCDM approaches. According to Mousavi et al (2022), TOPSIS is more accurate because it can apply discrete alternative challenges, which are the most crucial methods for resolving problems in the real world, and recognize viable alternatives right away.…”

Section: Discussionmentioning

confidence: 97%

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Debnath,

Sahariah,

Mazumdar

et al. 2023

Earth Syst Environ

View full text Add to dashboard Cite

Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.

show abstract

Section: Discussionmentioning

confidence: 97%

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Debnath,

Sahariah,

Mazumdar

et al. 2023

Earth Syst Environ

View full text Add to dashboard Cite

show abstract

“…The results showed the most prominent flood zones. Hadian et al [27] analysed the flood risks using TOPSIS and Attributive Border Approximation Area Comparison (MABAC). They also developed a map showing distribution of high-risk regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Urban Flood Risks of the Cities Using Entropy-Vikor Methods in Türki̇ye

DERSE,

GÖÇMEN POLAT

2024

KONJES

View full text Add to dashboard Cite

In recent years, there is growing interest for evaluation of urban flood risks of cities over the past decade due to rapid urbanization and climate change. The optimal flood risk assessment is strategically achieved not only with classical risk modelling approaches but also with holistic and comprehensive framework. This paper focuses on a detailed flood assessment providing risk database for policymakers and urban planners to decide the flood prone areas in Turkey. In this context, the Entropy based VIKOR (VIseKriterijumska Optimizacija Kompromisno Resenje) was provided to evaluate a range of flood risk criteria named number of floods, population density and number of buildings, flood protection area which are under the concept of risk dimension including “hazard, exposure and vulnerability” aspects. Computational results demonstrate that the provinces of Şanlıurfa, Ordu, Zonguldak and Van are assigned with higher urban risk values, respectively and the ranking of the cities was presented with different q values. The findings should support practitioners and researchers for land use planning and risk reduction works as the detailed flood risk evaluation was presented in terms of the flood management.

show abstract

“…For instance, Lee et al constructed an MCDM tool based on socio-economic development and climate change in a certain area of Seoul, South Korea to assess local flood vulnerability [19]. Sanaz Hadian et al drew a flood risk map of Mazandaran Province, Iran, based on MCDM and analyzed the local residents' ability to cope with flood disasters [20]. Kelly et al developed a large-scale flood risk assessment tool based on MCDM and analyzed flood risks in Australia [21].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Yuan,

Wang,

Wang

et al. 2024

Water

View full text Add to dashboard Cite

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological, and infrastructural. A hybrid model combining Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) From 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR were highly correlated with urban infrastructure development; however, the ecological resilience in the subsystem showed a fluctuating downward trend because of the influence of the surface area of lakes and rivers; (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

show abstract

Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness

Cited by 8 publications

References 70 publications

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

Assessment of Urban Flood Risks of the Cities Using Entropy-Vikor Methods in Türki̇ye

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Contact Info

Product

Resources

About