Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region

Duan, Yu; Xiong, Junnan; Cheng, Weiming; Wang, Nan; Li, Yi; He, Yufeng; Liu, Jun; He, Weiyi; Yang, Gang

doi:10.1007/s11069-021-04946-9

Cited by 23 publications

(8 citation statements)

References 87 publications

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“…It can be used for nonlinear and high-dimensional problem analysis when the sample data is small. Support vector machine regression is a branch of support vector machine, which has excellent learning performance and good generalization ability and has been widely used in system identification, vulnerability assessments (Duan et al, 2022) and prediction (Chen and Feng, 2020) in recent years. Therefore, it is also suitable for predicting the vulnerability of the WEFE nexus.…”

Section: Support Vector Machine Modelmentioning

confidence: 99%

Vulnerability evaluation and prediction of the water-energy-food-ecology nexus in the Yangtze River Economic Belt based on TOPSIS, neighborhood rough set and support vector machine

Pan

Chen

Liu

2022

Front. Environ. Sci.

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With the intensification of human activities, the vulnerability of water, energy, food and ecology systems has become more and more prominent, and the research on the vulnerability of the water-energy-food-ecology (WEFE) nexus is significant to realizing regional sustainable development. This study aims to make a comprehensive measurement of the vulnerability of the WEFE nexus in the Yangtze River Economic Belt (YREB) from 2008 to 2019, clarify the evolutionary trend of its vulnerability, and predict the vulnerability of each province (municipality) in the future, to formulate policies to reduce vulnerability. This study firstly constructs a vulnerability evaluation index system of the WEFE nexus based on the VSD framework and uses the neighborhood rough set (NRS) to reduce the attributes. Then, the TOPSIS model is used to evaluate the vulnerability of the WEFE nexus. Finally, by comparing the training accuracy of the random forest, decision tree and support vector machine (SVM) model, the SVM model is selected to predict the vulnerability of the WEFE nexus under different scenarios in 2025. The results show that the vulnerability of the WEFE nexus in the YREB showed a fluctuating downward trend from 2008 to 2019. The decline is faster in the middle and upper reaches, and slower in the lower reaches. In 2025, under a strong resource management scenario, the vulnerability of the provinces (municipalities) in the YREB will decrease significantly, while the weak and moderate resource management scenarios are not conducive to reducing vulnerability and even worsening. Therefore, in the future, the provinces (municipalities) in the upper, middle and lower reaches should make reasonable use of the resource endowments, take measurements actively according to local conditions, and continuously strengthen the management of water, energy, food and ecological resources, thus promoting the continuous improvement of the vulnerability of the WEFE nexus.

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Section: Support Vector Machine Modelmentioning

confidence: 99%

Vulnerability evaluation and prediction of the water-energy-food-ecology nexus in the Yangtze River Economic Belt based on TOPSIS, neighborhood rough set and support vector machine

Pan

Chen

Liu

2022

Front. Environ. Sci.

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“…More recently still, machine learning has been applied to evaluate hazard susceptibility, with models used including support vector machines (SVMs; Duan et al, 2022, Mohammady et al, 2019), random forest (RF; De Oliveira et al, 2019), bagging (Chen et al, 2019; Zhang et al, 2022), artificial neural networks (Ghasemian et al, 2022; Kalantar et al, 2018; Nikoobakht et al, 2022; Pham, Chandra Pal, et al, 2022; Rajabi et al, 2022; Shahabi et al, 2021), AdaBoost (ADB; Ha et al, 2022, Xiong et al, 2021), and XGBoost (Costache et al, 2022; Janizadeh et al, 2021). All models have their own advantages and disadvantages, and their performance depends on the input data used and the model's structure.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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“…One of the pressing requirements in studying the flooding events is the assessment of vulnerability. Researchers generally uses AHP (Dandapat and Panda 2017 ; Deepak et al 2020 ; Desalegn and Mulu 2020 ; Hoque et al 2019 ; Hussain et al 2021 ; Radmehr and Araghinejad 2015 ), analytical network process (Chukwuma et al 2021 ), Bayesian belief network (Abebe et al 2018 ), Weight of evidence-information value (Saha et al 2021 ), frequency ratio (FR) (Saha et al 2021 ; Sarkar and Mondal 2020 ), F-AHP (Duan et al 2021 ), and support vector machine (Duan et al 2021 ) for assessing flood vulnerability. Feloni et al ( 2020 ) created flood vulnerability maps of the Attica region in Greece using the AHP and fuzzy-analytical hierarchy process (F-AHP) methods.…”

Section: Introductionmentioning

confidence: 99%

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models

Senan

Ajin

Danumah

et al. 2022

Stoch Environ Res Risk Assess

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Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very highvulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.

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Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region

Cited by 23 publications

References 87 publications

Vulnerability evaluation and prediction of the water-energy-food-ecology nexus in the Yangtze River Economic Belt based on TOPSIS, neighborhood rough set and support vector machine

Vulnerability evaluation and prediction of the water-energy-food-ecology nexus in the Yangtze River Economic Belt based on TOPSIS, neighborhood rough set and support vector machine

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models

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