Automation and Optimization of Pipeline Leak Detection Using ANN-XGBoost Algorithms and PLC Integration

Water leakage in distribution networks poses significant challenges, including resource wastage and increased operational costs. This paper presents the ANN-XGBoost algorithm, an innovative approach that combines Artificial Neural Networks (ANN) with Extreme Gradient Boosting (XGBoost) to enhance water leak detection in water distribution networks (WDNs). By leveraging multi-dimensional sensor data, specifically pressure, flow rate, and temperature, the algorithm achieves high detection accuracy and reduces false alarms. The simulation was conducted using EPANET, a widely recognized software for modeling water distribution systems. Simulation results demonstrate that the ANN-XGBoost model outperforms traditional models, achieving a detection accuracy of up to $98 \%$, while the individual ANN and XGBoost models reached maximum accuracies of $95 \%$ and $89 \%$, respectively. Precision metrics further indicate significant improvements, with the ANNXGBoost model achieving a precision of $\mathbf{8 5 \%}$ compared to $\mathbf{7 4 \%}$ and $80 \%$ for ANN and XGBoost, respectively. Additionally, the ANN-XGBoost model achieved a False Positive Rate (FPR) of only $5 \%$, significantly lower than the $12 \%$ and $10 \%$ rates observed in the ANN and XGBoost models, respectively. The ANN-XGBoost algorithm effectively integrates Machine Learning (ML) techniques with IoT technologies, facilitating real-time monitoring and automated responses to potential leaks.