期刊
REMOTE SENSING
卷 13, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/rs13245068
关键词
landslide susceptibility assessment; power transmission network; slope terrain unit; random forest; logistic regression
This study compared different data-driven methods for landslide susceptibility assessment along power transmission network, using a case study from China. The findings showed that machine learning techniques are suitable for assessing landslide susceptibility along grid cell units of the power network, while multivariate statistical logistic regression methods perform better on heterogeneous slope terrain units with reduced unit count. High model predictive performances do not guarantee high plausibility and applicability of susceptibility maps generated.
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.
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