Developing Machine Learning Models for Optimal Design of Water Distribution Networks Using Graph Theory-Based Features

This study presents an innovative data-driven approach for the optimal design of water distribution networks (WDNs). The methodology comprises five key stages: Generation of 600 synthetic WDNs with diverse properties, optimized to determine optimal component diameters; Extraction of 80 topological and hydraulic features from the optimized WDNs using graph theory; Preprocessing and preparation of the extracted features using established data science methods; Application of six feature selection methods (Variance Threshold, k-best, chi-squared, Light Gradient-Boosting Machine, Permutation, and Extreme Gradient Boosting) to identify the most relevant features for describing optimal diameters; and Integration of the selected features with four machine learning models (Random Forest, Support Vector Machine, Bootstrap Aggregating, and Light Gradient-Boosting Machine), resulting in 24 ensemble models. The Extreme Gradient Boosting-Light Gradient-Boosting Machine (Xg-LGB) model emerged as the optimal choice, achieving R² and RMSE values of 0.98 and 0.02, respectively. When applied to a benchmark WDN, this model demonstrated high accuracy in predicting optimal diameters, with R² and RMSE values of 0.94 and 0.06, respectively. These results underscore the potential of the developed model for accurate and efficient optimal design of WDNs.