  In this paper, we present a nodal hydraulic head estimation methodology for
water distribution networks (WDN) based on an Unscented Kalman Filter (UKF)
scheme with application to leak localization. The UKF refines an initial
estimation of the hydraulic state by considering the prediction model, as well
as available pressure and demand measurements. To this end, it provides
customized prediction and data assimilation steps. Additionally, the method is
enhanced by dynamically updating the prediction function weight matrices.
Performance testing on the Modena benchmark under realistic conditions
demonstrates the method's effectiveness in enhancing state estimation and
data-driven leak localization.
