Abstract
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.
Highlights
When it comes to the Logistic regression (LR) models with different mapping unit, the results reveal that the proportion of landslides in each level of susceptibility region are quite close
The prediction ability of the RF model was significantly better than that of the LR model, sometimes, the landslide susceptibility mapping (LSM) in slope unit (SU) form may perform better in result exhibition and practical application
Using different data-driven methods and two different mapping units, we presented several landslide susceptibility assessment results in an area along power transmission lines
Summary
As climate change intensified and energy demand expanded [1], the malfunctions of power network frequently occurred in recent years, such as the massive outage in. In the United States, in 2021 [2,3], and the severe electricity shortage in northeast. China since September 2021 [4]. As the main frame of power network, high-voltage transmission lines must span wide areas with different geographic and climatic features. Power transmission infrastructures are usually built on mountainous and hilly area in order to avoid the mutual interaction with human activity.
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