Short term nodal pressure prediction using hybrid machine learning regression for an urban water distribution network

Accurate prediction of nodal pressure is necessary for effectual scheduling and enhancing pipeline reliability in water distribution systems. In this study, a Python Jupyter Notebook was used to build a predictive model. Nodal pressure analysis was executed at four-hour intervals across all 99 nodes in the Peroorkada urban water distribution network. The nodal pressure prediction was achieved using sixteen machine learning algorithms, including three hybrid/stacking regressors namely multi-layer perceptron (MLP), stochastic gradient descent (SGD) and K-nearest neighbours (KNN). The stacking regressor models consistently outperformed individual prediction models, with the MLP-hybrid regressor attaining the best results with a high regression score of 0.995 and lowest mean squared error of 0.36. The findings suggest that employing hybrid machine learning models, particularly the MLP-hybrid regressor, can significantly improve the accuracy of nodal pressure prediction in urban water distribution networks. Enhanced prediction capabilities can establish better scheduling and support earlier detection of leaks.