Susceptibility Assessment for Landslide Initiated along Power Transmission Lines
The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great sig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/24/5068 |
| _version_ | 1827669824688357376 |
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| author | Shuhao Liu Kunlong Yin Chao Zhou Lei Gui Xin Liang Wei Lin Binbin Zhao |
| author_facet | Shuhao Liu Kunlong Yin Chao Zhou Lei Gui Xin Liang Wei Lin Binbin Zhao |
| author_sort | Shuhao Liu |
| collection | DOAJ |
| description | The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation. |
| first_indexed | 2024-03-10T03:11:29Z |
| format | Article |
| id | doaj.art-4ddc5c4345134ca98da86fe0660133b0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:11:29Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4ddc5c4345134ca98da86fe0660133b02023-11-23T10:24:22ZengMDPI AGRemote Sensing2072-42922021-12-011324506810.3390/rs13245068Susceptibility Assessment for Landslide Initiated along Power Transmission LinesShuhao Liu0Kunlong Yin1Chao Zhou2Lei Gui3Xin Liang4Wei Lin5Binbin Zhao6Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaResearch Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir, Chongqing 404100, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaThe power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation.https://www.mdpi.com/2072-4292/13/24/5068landslide susceptibility assessmentpower transmission networkslope terrain unitrandom forestlogistic regression |
| spellingShingle | Shuhao Liu Kunlong Yin Chao Zhou Lei Gui Xin Liang Wei Lin Binbin Zhao Susceptibility Assessment for Landslide Initiated along Power Transmission Lines Remote Sensing landslide susceptibility assessment power transmission network slope terrain unit random forest logistic regression |
| title | Susceptibility Assessment for Landslide Initiated along Power Transmission Lines |
| title_full | Susceptibility Assessment for Landslide Initiated along Power Transmission Lines |
| title_fullStr | Susceptibility Assessment for Landslide Initiated along Power Transmission Lines |
| title_full_unstemmed | Susceptibility Assessment for Landslide Initiated along Power Transmission Lines |
| title_short | Susceptibility Assessment for Landslide Initiated along Power Transmission Lines |
| title_sort | susceptibility assessment for landslide initiated along power transmission lines |
| topic | landslide susceptibility assessment power transmission network slope terrain unit random forest logistic regression |
| url | https://www.mdpi.com/2072-4292/13/24/5068 |
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