Mapping of highway slope hazard susceptibility based on InSAR and machine learning: a pilot study toward road infrastructure resilience
Source abstract
Abstract
Highway slope hazard susceptibility monitoring and prediction is of great importance for highway disaster risk management which corresponds to highway infrastructure resilience. This paper proposes a method for predicting slope hazard susceptibility within the highway area. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), geospatial analysis and machine learning algorithms are employed for achieving the purpose. Specifically, 11 influencing factors of the study area are selected and the data are collected. Sensitivity of the influencing factors is verified using matrix scatter plot, Spearman coefficient test and one-way ANOVA test. The ANN, SVM and CatBoost models are constructed to predict the slope hazard susceptibility. Among them, CatBoost shows the best performance on the testing set with an AUC value of 0.85. The findings of this study assist in evaluating the resilience of road infrastructure and hazard susceptibility of highway slopes.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Landslides and related hazards · Geotechnical Engineering and Analysis · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Southeast University
Funding
National Key Research and Development Program of China · National Natural Science Foundation of China
References (40)
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