Estimation of Node Pressures in Water Distribution Networks by Gaussian Process Regression

This paper proposes a method to predict pressures in all nodes of a water distribution network (WDN) by Gaussian process regression (GPR) from pressure measurements in a subset of selected nodes. The pressure sensors are placed in the nodes where, together, they capture the maximum pressure variance and also have a minimum sensitivity to measurement noise. As a case study, the proposed method was tested on a dataset obtained from simulations with the hydraulic model of the Hanoi WDN. Using only three pressure sensors, the GPR estimation error in the pressures of the unmeasured nodes are comparable to the error due to measurement noise in physical pressure sensors.