Evaluation of geological disaster susceptibility of transmission lines under different grid resolutions

Abstract

Objective The safe operation of transmission lines is of great significance for national economic construction and development, but there were few studies on the evaluation of geological hazards susceptibility to transmission lines. Methods This study focuses on the Beijing-Tianjin-Hebei region as an example, where eight index factors, including elevation, slope, aspect, terrain relief, stratigraphic lithology, distance from fault, distance from water system, and land use type were selected. The frequency ratio method was used to classify each index factor to construct a susceptibility evaluation system.Then used different machine learning models and grid of different spatial resolutions as evaluation units to evaluate the susceptibility of the study area.Finally, the machine learning model with the highest accuracy and the traditional Analytic Hierarchy Process (AHP) were selected to complete the susceptibility zoning map of the study area. Results The research results show that the Bayesian Network model (Bayesian Network, BN) had the best application effect and the strongest model performance in the susceptibility evaluation of regional transmission lines, and the maximum AUC value was 0.876. The BN model outperformed the traditional AHP model, displaying superior precision in susceptibility mapping in the study area. Conclusion In addition, emplpying 50 m grid as the evaluation unit had achieved the best application effect in the evaluation of transmission line geological disaster susceptibility, which provided ideas and references for transmission line geological disaster evaluation and grid resolution selection.

Keywords

• transmission line
• geological disaster
• grid resolution
• machine learning
• susceptibility evaluation