The assessment of scour depth downstream of weirs holds paramount importance in ensuring the structural stability of these hydraulic structures. This study presents groundbreaking experimental investigations highlighting the innovative use of baffles to enhance energy dissipation and mitigate scour in the downstream beds of rectangular piano key weirs (RPKWs) and trapezoidal piano key weirs (TPKWs). By leveraging three state-of-the-art supervised machine learning algorithms—multi-layer perceptron (MLP), extreme gradient boosting (XGBoost), and support vector regression (SVR)—to estimate scour hole parameters, this research showcases significant advancements in predictive modeling for scour analysis. Experimental results reveal that the incorporation of baffles leads to a remarkable 18–22% increase in energy dissipation and an 11–14% reduction in scour depth for both RPKWs and TPKWs. Specifically, introducing baffles in RPKWs resulted in a noteworthy 26.7% reduction in scour hole area and a 30.3% decrease in scour volume compared to RPKWs without baffles. Moreover, novel empirical equations were developed to estimate scour parameters, achieving impressive performance metrics with an average R² = 0.951, RMSE = 0.145, and MRPE = 4.429%. The MLP models demonstrate superior performance in predicting maximum scour depth across all scenarios with an average R² = 0.988, RMSE = 0.035, and MRPE = 1.036%. However, the predictive capabilities varied when estimating weir toe scour depth under diverse circumstances, with the XGBoost model proving more accurate in scenarios involving baffled TPKWs with R² = 0.965, RMSE = 0.048, and MRPE = 2.798% than the MLP and SVR models. This research underscores the significant role of baffles in minimizing scouring effects in TPKWs compared to RPKWs, showcasing the potential for improved design and efficiency in water-management systems.

Occurrence of high pressure in water distribution networks (WDNs) may generally result in leakage, bursting, and overconsumption. Temporal and spatial variations in pressure values cause dissatisfaction of consumers and distrust of WDNs. Pressure management is an operational strategy to improve the reliability of WDNs. This strategy should be considered as a cost-savings practice due to the fact that operating costs are a significant proportion of WDN costs. In the present study, the surplus pressure in a WDN, consumed by turbines, is used to generate energy and compensate for some of the costs. Due to the high cost and specialized construction of the turbines, the pumps working in reverse or pump-as-turbines (PATs) have been employed in the WDNs instead of turbines. This investigation aims to improve the network performance by installing PATs with the appropriate number, type, and time schedule in the right locations. In this regard, the optimal schedules for pumping and using pressure-reducing valves (PRVs) have been considered. A numerical model was developed to optimize consumers’ satisfaction and costs as objective functions. The efficiency of the proposed model was evaluated by selecting a case study with overpressure, and additionally, the objective functions were optimized by using two scenarios. The results associated with the first scenario indicated a reduction of network costs by up to 90% over its life cycle as well as an increase in network reliability. Through the second scenario, multiobjective optimization provided a trade-off diagram as the final solution for choosing various solutions with different cost levels and reliabilities.

This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than “balance point” to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC–ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.