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<p>Urban pipeline infrastructure plays a vital role in ensuring the operational efficiency and service reliability of modern utility systems, especially in industrial regions. While previous studies have focused primarily on pipe failure prediction, limited research has addressed the forecasting of key pipeline performance indicators such as velocity, pressure, and head loss within the context of infrastructure asset management. This study investigates the performance of advanced machine learning (ML) models, PSO-ANN, Genetic CNN, Quantum SVR, Fuzzy Logic Tree, and Bayesian GPR, in predicting three critical output variables: velocity, head loss, and pressure. A dataset comprising 91 instances with geometric and hydraulic descriptors was employed, and descriptive statistics revealed significant variability in flow-dependent parameters. SHAP-based sensitivity analysis highlighted elevation (0.9287) as the dominant factor for pressure prediction, while flow rate (0.4574) and diameter (0.2273) strongly influenced head loss. For velocity, flow rate (0.1139) emerged as the most influential, though other parameters also contributed, justifying their inclusion in the modeling framework. The models were trained using data from the Gadhra Water Distribution Network (District Metered Area-03) in East Singhbhum, Jamshedpur, India. Model evaluation was conducted using R², RMSE, MAE, and MAPE. Results demonstrated a clear performance hierarchy, with Bayesian GPR and Fuzzy Logic Tree exhibiting superior accuracy and stability (R² ≥ 0.98, RMSE ≤ 0.06, MAPE ≤ 0.13), whereas PSO-ANN and Genetic CNN showed relatively weaker performance. The near-perfect R² observed for Fuzzy Logic Tree reflects the small dataset size and its high capacity, highlighting that generalization may be limited in larger or unseen datasets. The analysis of regressor plots, residual distributions, and normalized accuracy matrices further validated these findings. Overall, the study establishes Bayesian GPR and Fuzzy Logic Tree as robust predictive tools for hydraulic modeling while acknowledging dataset constraints that may affect generalization.</p>

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Smart urban utility management using ML-based prediction of pipeline performance in the sustainable water supply network of Jamshedpur industrial city

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Kumar Shubham,
  • Abu Rashid,
  • Monika Simon,
  • K. V. N. S. Raviteja,
  • Akhilesh Kumar Yadav

摘要

Urban pipeline infrastructure plays a vital role in ensuring the operational efficiency and service reliability of modern utility systems, especially in industrial regions. While previous studies have focused primarily on pipe failure prediction, limited research has addressed the forecasting of key pipeline performance indicators such as velocity, pressure, and head loss within the context of infrastructure asset management. This study investigates the performance of advanced machine learning (ML) models, PSO-ANN, Genetic CNN, Quantum SVR, Fuzzy Logic Tree, and Bayesian GPR, in predicting three critical output variables: velocity, head loss, and pressure. A dataset comprising 91 instances with geometric and hydraulic descriptors was employed, and descriptive statistics revealed significant variability in flow-dependent parameters. SHAP-based sensitivity analysis highlighted elevation (0.9287) as the dominant factor for pressure prediction, while flow rate (0.4574) and diameter (0.2273) strongly influenced head loss. For velocity, flow rate (0.1139) emerged as the most influential, though other parameters also contributed, justifying their inclusion in the modeling framework. The models were trained using data from the Gadhra Water Distribution Network (District Metered Area-03) in East Singhbhum, Jamshedpur, India. Model evaluation was conducted using R², RMSE, MAE, and MAPE. Results demonstrated a clear performance hierarchy, with Bayesian GPR and Fuzzy Logic Tree exhibiting superior accuracy and stability (R² ≥ 0.98, RMSE ≤ 0.06, MAPE ≤ 0.13), whereas PSO-ANN and Genetic CNN showed relatively weaker performance. The near-perfect R² observed for Fuzzy Logic Tree reflects the small dataset size and its high capacity, highlighting that generalization may be limited in larger or unseen datasets. The analysis of regressor plots, residual distributions, and normalized accuracy matrices further validated these findings. Overall, the study establishes Bayesian GPR and Fuzzy Logic Tree as robust predictive tools for hydraulic modeling while acknowledging dataset constraints that may affect generalization.