Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) at the Pengyang county, Northwest China

Mao, Zhengjun; Shi, Shuojie; Li, Huan; Zhong, Jiaxin; Sun, Jiewen

doi:10.1007/s12665-022-10193-3

Cited by 21 publications

(10 citation statements)

References 68 publications

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“…Based on the literature review, nine commonly used base factors are selected for this study: elevation, slope, aspect, curvature, lithology, distance to faults, TWI, NDVI, and multi-year average rainfall 6 , 21 , 22 . To avoid correlation and multicollinearity among these factors, diagnostic methods like Pearson Correlation Coefficient 23 , Variance Inflation Factor and Tolerance 24 , and Relief-F algorithms 25 are employed. The final diagnosed landslide evaluation factors are shown in Fig.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Yu,

Chen

2024

Sci Rep

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Landslides, recognized as a significant global natural disaster, necessitate an exploration of the impact of various resolution types in sampling strategies on Landslide Susceptibility Mapping (LSM) results. This study focuses on the segment from Zigui to Badong within the Three Gorges Reservoir Area, utilizing two resolution types: sampling resolution and spatial resolution, The Support Vector Machine (SVM) is employed to obtain LSM results, which are then analyzed using Receiver Operating Characteristic (ROC) curve, specific category accuracy and statistical methods. Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used to verify the reliability of the results. Additionally, five common machine learning models, including Logistic Regression (LR), are used to conduct experiments on four sampling resolutions (10 m,30 m,50 m and 70 m) to further investigate the effect of sampling resolution on LSM results. These are evaluated using a comprehensive quantitative method. The results reveal that increasing spatial resolution improves the prediction accuracy, while increasing sampling resolution produces a contrary effect. Furthermore, the impact of spatial resolution on LSM results is more pronounced than that of sampling resolution. Finally, Fanjiaping landslide and Huangtupo landslide are selected as references for comparative analysis, with the results aligning with engineering reality.

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Section: Study Area and Data Sourcesmentioning

confidence: 99%

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Yu,

Chen

2024

Sci Rep

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“…Here, triangular fuzzy numbers (TFN) are used as it is comprehensive for both qualitative and quantitative analysis. Further, with triangular fuzzy numbers, it is easy to give appropriate rating for the factors under consideration [72]. Individual response from the five experts were collected and aggregated.…”

Section: ) Finalization and Evaluation Of Barriers To Ce Practices In...mentioning

confidence: 99%

Analyzing the Challenges to Circular Economy in Indian Fashion Industry

et al. 2023

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The fashion industry, during production and post-consumption, has major adverse environmental impacts. With the recent circular economy (CE) practices initiative, discarded fashion materials are reused to recover value from the waste. However, the CE practices initiative have not been well-received by society. The role of CE practices is very crucial in fashion industry as it helps in lowering the environmental burden caused by the discarded fashion materials, which in turn help to achieve the sustainable development goals (SDG). This study aims to collect and evaluate the challenges restricting the preference for CE practices initiative in Indian fashion industry. Based on literature review and inputs from the fashion industry experts, the study identified twenty-one challenges, in seven categories (Industrial management, Labour, Material, Regulations, Knowledge, Collaboration, and Infrastructure). An integrated multi-criteria decisionmaking (MCDM) approach comprising fuzzy Decision-Making Trial and Evaluation Laboratory Model (DEMATEL), Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to evaluate the challenges. The outcome of the study identifies costly raw materials, absence of certifications, problem in collection and separation, weak technical know-how, and absence of shared vision as the top five challenges to CE practices in Indian fashion industry. Further, the revelation of the causal interrelationship among the challenges helps the industrial management in taking appropriate steps to avert the challenges.INDEX TERMS Circular economy (CE), fashion industry, fuzzy decision-making trial and evaluation laboratory model (FDEMATEL), India.

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“…The GWP-influencing factors optimized selection analysis and models application were founded on the normalized frequency ratio (NFR) determination, which has recently been a recommended step for unifying the input data type importance to the varied factors [20,51]. Therefore, the frequency ratio (FR) (Equation ( 4)) was assigned for the GWP-influencing factors' subclass in the sense of defining the relationship between the water withdrawal locations (i.e., GWP) and the feature (i.e., GWP-influencing factors) [6].…”

Section: Gwp-influencing Factors Optimized Selection Analysismentioning

confidence: 99%

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

et al. 2023

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Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.

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Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) at the Pengyang county, Northwest China

Cited by 21 publications

References 68 publications

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Analyzing the Challenges to Circular Economy in Indian Fashion Industry

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

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