An optimized model for dual-point leakage monitoring and localization in fire protection pipe networks based on Bayesian Optimization-Light Gradient Boosting Machine

The fire protection pipeline network is a critical infrastructure for ensuring fire safety, and its reliability directly influences the effectiveness of fire suppression. These networks are susceptible to corrosion, aging, or cracking-induced leakage. Traditional single-point leakage models struggle to accurately capture the complex characteristics of multi-point leak scenarios, which often result in greater water loss and more significant pressure drops. Therefore, it is essential to develop specialized methods for the effective identification and accurate localization of multi-point leaks, which are commonly represented by dual-point leaks. This study proposes a dual-point leak localization method based on a Bayesian Optimization and Light Gradient Boosting Machine (BO-LightGBM) model for fire protection pipe networks. This approach integrates experimental data with numerical simulations to create a dataset of dual-leak scenarios under varying operating conditions. The Bayesian optimization algorithm is used to automatically determine the optimal hyperparameters for the LightGBM model, which is then trained as the final BO-LightGBM localization model. Testing on simulated datasets shows that the proposed model achieves 96.11% accuracy. The BO-LightGBM model demonstrates superior performance compared to other localization models developed using mainstream machine learning algorithms. In conclusion, the BO-LightGBM method presented in this study effectively detects and localizes dual-point leaks in fire protection pipeline networks. It significantly reduces manual monitoring requirements and addresses limitations such as poor real-time performance and data imbalance in existing methods. This technology provides strong support for advancing intelligent fire protection systems.