Stability Prediction Model of Transmission Tower Slope Based on ISCSO-SVM

Abstract

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure the reliability of transmission line operations, this paper presents a stability prediction model for transmission tower slopes based on the Improved Sand Cat Swarm Optimization (ISCSO) algorithm and Support Vector Machine (SVM). The ISCSO algorithm is enhanced with dynamic reverse learning and triangular wandering strategies, which are then used to optimize the kernel and penalty parameters of the SVM, resulting in the ISCSO-SVM prediction model. In this study, a typical transmission tower slope in southern China is used as a case study, with the transmission tower slope database generated through orthogonal experimental design and Geo-studio simulations. In addition to traditional input features, an additional input—transmission tower catchment area—is incorporated, and the stable state of the transmission tower slope is set as the predicted output. The results demonstrate that the ISCSO-SVM model achieves the highest prediction accuracy, with the smallest errors across all metrics. Specifically, compared to the standard SVM, the MAPE, MAE, and RMSE values are reduced by 70.96%, 71.41%, and 57.37%, respectively. The ISCSO-SVM model effectively predicts the stability of transmission tower slopes, thereby ensuring the safe operation of transmission lines.