Articles | Volume 20, issue 5
https://doi.org/10.5194/nhess-20-1287-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-1287-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 May 2020
Research article |
| 14 May 2020
| 14 May 2020
Classification and susceptibility assessment of debris flow based on a semi-quantitative method combination of the fuzzy C-means algorithm, factor analysis and efficacy coefficient
Zhu Liang
College of Construction Engineering, Jilin University, 130000 Changchun, People's Republic of China
College of Construction Engineering, Jilin University, 130000 Changchun, People's Republic of China
Songling Han
College of Construction Engineering, Jilin University, 130000 Changchun, People's Republic of China
Kaleem Ullah Jan Khan
College of Construction Engineering, Jilin University, 130000 Changchun, People's Republic of China
Yiao Liu
College of Construction Engineering, Jilin University, 130000 Changchun, People's Republic of China
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Cited
21 citations as recorded by crossref.
- A progressive framework combining unsupervised and optimized supervised learning for debris flow susceptibility assessment Y. Liu et al. 10.1016/j.catena.2023.107560
- Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests Q. Zhang et al. 10.3390/su142416692
- Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Z. Liang et al. 10.1007/s00477-020-01893-y
- Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning Z. Liang et al. 10.3390/su14106110
- Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet Z. Liang et al. 10.3390/app13127276
- A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning R. Gao et al. 10.3390/rs14194829
- Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin, China Y. Zhou et al. 10.1007/s11629-023-8500-0
- A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping Z. Liang et al. 10.3390/rs13081464
- Unified Plasticity Potential of Soils B. Firincioglu & H. Bilsel 10.3390/app13137889
- Assessment of Debris Flow Risk Factors Based on Meta-Analysis—Cases Study of Northwest and Southwest China Y. Wang et al. 10.3390/su12176841
- Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping H. Hu et al. 10.3390/ijgi10100639
- The Reform of Engineering Professional Online Education Courses by Artificial Intelligence and Wireless Network Technology in the Context of Engineering Certification C. Che et al. 10.1155/2022/3822931
- Numerical Investigation of Bedding Rock Slope Potential Failure Modes and Triggering Factors: A Case Study of a Bridge Anchorage Excavated Foundation Pit Slope S. Han & C. Wang 10.3390/app14166891
- Dynamic Response Analysis of Retaining Dam under the Impact of Solid-Liquid Two-Phase Debris Flow Based on the Coupled SPH-DEM-FEM Method B. Li et al. 10.1155/2020/6635378
- Debris Flow Classification and Risk Assessment Based on Combination Weighting Method and Cluster Analysis: A Case Study of Debris Flow Clusters in Longmenshan Town, Pengzhou, China Y. Li et al. 10.3390/app13137551
- A Research on Susceptibility Mapping of Multiple Geological Hazards in Yanzi River Basin, China R. Gao et al. 10.3390/ijgi10040218
- Dynamic process of a typical slope debris flow: a case study of the wujia gully, Zengda, Sichuan Province, China Y. Shunyu et al. 10.1007/s11069-021-05194-7
- A debris flow susceptibility mapping study considering sample heterogeneity R. Gao et al. 10.1007/s12145-024-01453-w
- Exploring the potential relationship between the occurrence of debris flow and landslides Z. Liang et al. 10.5194/nhess-21-1247-2021
- Assessing the Susceptibility of the Xiangka Debris Flow Using Analytic Hierarchy Process, Fuzzy Comprehensive Evaluation Method, and Cloud Model Y. Li et al. 10.3390/su16135392
- Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds R. Gao et al. 10.1007/s11629-020-6471-y
21 citations as recorded by crossref.
- A progressive framework combining unsupervised and optimized supervised learning for debris flow susceptibility assessment Y. Liu et al. 10.1016/j.catena.2023.107560
- Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests Q. Zhang et al. 10.3390/su142416692
- Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Z. Liang et al. 10.1007/s00477-020-01893-y
- Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning Z. Liang et al. 10.3390/su14106110
- Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet Z. Liang et al. 10.3390/app13127276
- A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning R. Gao et al. 10.3390/rs14194829
- Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin, China Y. Zhou et al. 10.1007/s11629-023-8500-0
- A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping Z. Liang et al. 10.3390/rs13081464
- Unified Plasticity Potential of Soils B. Firincioglu & H. Bilsel 10.3390/app13137889
- Assessment of Debris Flow Risk Factors Based on Meta-Analysis—Cases Study of Northwest and Southwest China Y. Wang et al. 10.3390/su12176841
- Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping H. Hu et al. 10.3390/ijgi10100639
- The Reform of Engineering Professional Online Education Courses by Artificial Intelligence and Wireless Network Technology in the Context of Engineering Certification C. Che et al. 10.1155/2022/3822931
- Numerical Investigation of Bedding Rock Slope Potential Failure Modes and Triggering Factors: A Case Study of a Bridge Anchorage Excavated Foundation Pit Slope S. Han & C. Wang 10.3390/app14166891
- Dynamic Response Analysis of Retaining Dam under the Impact of Solid-Liquid Two-Phase Debris Flow Based on the Coupled SPH-DEM-FEM Method B. Li et al. 10.1155/2020/6635378
- Debris Flow Classification and Risk Assessment Based on Combination Weighting Method and Cluster Analysis: A Case Study of Debris Flow Clusters in Longmenshan Town, Pengzhou, China Y. Li et al. 10.3390/app13137551
- A Research on Susceptibility Mapping of Multiple Geological Hazards in Yanzi River Basin, China R. Gao et al. 10.3390/ijgi10040218
- Dynamic process of a typical slope debris flow: a case study of the wujia gully, Zengda, Sichuan Province, China Y. Shunyu et al. 10.1007/s11069-021-05194-7
- A debris flow susceptibility mapping study considering sample heterogeneity R. Gao et al. 10.1007/s12145-024-01453-w
- Exploring the potential relationship between the occurrence of debris flow and landslides Z. Liang et al. 10.5194/nhess-21-1247-2021
- Assessing the Susceptibility of the Xiangka Debris Flow Using Analytic Hierarchy Process, Fuzzy Comprehensive Evaluation Method, and Cloud Model Y. Li et al. 10.3390/su16135392
- Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds R. Gao et al. 10.1007/s11629-020-6471-y
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Short summary
The present study built a semi-quantitative classification and susceptibility assessment method for a study area, combining multiple mathematical methods and 3S technologies. The results have been verified with field investigation and other evaluation methods. Different methods have their own advantages and disadvantages, and some methods are complementary to a certain extent, so it is desirable to enhance the rationality of the application through the combination of multiple methods.
The present study built a semi-quantitative classification and susceptibility assessment method...
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