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Installation of line surge arresters on transmission towers can significantly improve the line lightning performance. However, it is not always economically beneficial to install the line surge arresters on every tower in the network. This paper proposes the method for optimal placement of line surge arresters that minimizes the overall risk of lightning related outages and disturbances, while staying within the required budgetary limits. A variety of data sources was used: utility asset management, geographical information system, lightning detection network, historical weather and weather forecasts, vegetation and soil properties. The proposed solution is focused on predicting the risk of transmission line insulators experiencing an insulation breakdown due to the accumulated deterioration over time and an instant impact of a given lightning strike. The linear regression prediction-based algorithm observes the impact of various historical events on each individual component. In addition, the spatial distribution of various impacts is used to enhance the predictive performance of the algorithm. The developed method is fully automated, making it a unique large scale automated decision-making risk model for real-time management of the transmission line lightning protection performance. Based on the observation of risk tracking and prediction, the zones with highest probability of lightning caused outages are identified. Then the optimization algorithm is applied to determine the best placement strategy for the limited number of line surge arresters that would provide the highest reduction in the overall risk for the network. Economic factors are taken into account in order to develop installation schedule that would enable economically efficient management of line lightning protection performance for utilities