Water distribution network leak localization with histogram-based gradient boosting

Abstract

Accurate and rapid leak localization in water distribution networks is extremely important as it prevents further loss of water and reduces water scarcity. A framework for identifying relevant leak event parameters such as the leak location, leakage area, and start time is presented in this paper. Firstly, the proposed data-driven methodology consists of acquiring pressure data at nodes in the network through hydraulic simulations by randomly changing the leak event initial conditions (leak location, area, and start time). Pressure uncertainties are added to the sensor measurements in order to make the problem more realistic. Secondly, the acquired data are then used to train, test, and validate a machine learning model in order to predict the relevant parameters. The random forest and the histogram-based gradient boosting machine learning algorithms are investigated and compared for the leak detection problem. The proposed approach with the histogram-based gradient boosting algorithm shows high accuracy in predicting the true leak location. HIGHLIGHTS A machine learning-based framework for water network leak localization is presented.; ML models are trained with data generated by the WNTR simulator.; Histogram-based gradient boosting outperforms the random forests algorithm.; HGB achieved a leak node search space reduction of 92% with added pressure uncertainty.; High correlation between pressure measurements and leak area (R2 = 75%) and leak start time (R2 = 97%).;

Keywords

• data farming
• histogram-based gradient boosting
• leak localization
• random forests
• water distribution networks
• wntr