Advanced light gradient boosting machine-based model for accurate water leakage localization in fire protection pipe network

Fire water systems serve as a core component of the city's lifeline program. Its safety and reliability are directly related to the safety of people's lives and property and the operational order of the city. However, firefighting pipelines are often at risk of leakage due to aging, mechanical damage, corrosion, and circumferential weld cracking. Therefore, effective methods for detecting and locating pipeline leaks are essential. Existing methods are often complex, time-consuming, and unreliable. This paper proposes an integrated approach for leak localization in fire-distance fire protection pipelines by constructing a light gradient boosting machine-based model. The model simultaneously addresses the challenges of multi-categorical leak localization with unbalanced data and enhances the accuracy and reliability of leakage detection in long-distance pipelines. The model identifies leak locations through a two-step process and demonstrates high performance, achieving an accuracy rate of over 90%. Furthermore, it exhibits strong generalization capability and robustness. This method significantly enhances the accuracy of leak detection in long-distance fire protection pipelines, improves the operational reliability of fire water networks, and enables staff to make prompt maintenance decisions.