Predicting deep well pump performance with machine learning methods during hydraulic head changes.

In this study, machine learning techniques were employed to estimate and predict the system efficiency of a pumping plant at various hydraulic head levels. The measured parameters, including flow rate, outlet pressure, drawdown, and power, were used for estimating the system efficiency. Two approaches, Approach-I and Approach-II, were utilized. Approach-I incorporated additional parameters such as hydraulic head, drawdown, flow, power, and outlet pressure, while Approach-II focused solely on hydraulic head, outlet pressure, and power. Seven machine learning algorithms were employed to model and predict the efficiency of the pumping plant. The decrease in the hydraulic head by 125 cm resulted in a reduction in the pump system efficiency by 6.45 %, 8.94 %, and 13.8 % at flow rates of 40, 50, and 60 m h, respectively. Among the algorithms used in Approach-I, the artificial neural network, support vector machine regression, and lasso regression exhibited the highest performance, with R values of 0.995, 0.987, and 0.985, respectively. The corresponding RMSE values for these algorithms were 0.13 %, 0.23 %, and 0.22 %, while the MAE values were 0.11 %, 0.2 %, and 0.32 %, and the MAPE values were 0.22 %, 0.5 %, and 0.46.% In Approach-II, the artificial neural network model once again demonstrated the best performance with an R value of 0.996, followed by the support vector machine regression (R = 0.988) and the decision tree regression (R = 0.981). Overall, the artificial neural network model proved to be the most effective in both approaches. These findings highlight the potential of machine learning techniques in predicting the efficiency of pumping plant systems.