[Objective]The safe operation of transmission lines is of great significance for national economic con-struction and development,but there were few studies on the evaluation of geological hazards susceptibility to trans-mission lines.[Methods]This study focuses on the Beijing-Tianjin-Hebei region as an example,where eight in-dex 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 spa-tial resolutions as evaluation units to evaluate the susceptibility of the study area.Finally,the machine learning mod-el 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 e-valuation 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 geolog-ical disaster evaluation and grid resolution selection.
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