Online Modelling of Water Distribution System Using Data Assimilation

Abstract This paper applies Data Assimilation (DA) methods to a Water Distribution System Model to improve the real- time estimation of water demand, and hydraulic system states. A time series model is used to forecast water demands which are used to drive the hydraulic model to predict the future system state. Both water demands and water demand model parameters are corrected via DA methods to update the system state. The results indicate that DA methods improved offline hydraulic modelling predictions. Of the DA methods, the Ensemble Kalman Filter outperformed the Kalman Filter in term of updating demands and water demand model parameters.

[1]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[2]  R. Quentin Grafton,et al.  Water Resources Planning and Management: Urban water supply and management , 2011 .

[3]  Joaquín Izquierdo,et al.  Predictive models for forecasting hourly urban water demand , 2010 .

[4]  K. Adamowski,et al.  Short‐term municipal water demand forecasting , 2005 .

[5]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[6]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[7]  T. McMahon,et al.  Forecasting operational demand for an urban water supply zone , 2002 .

[8]  Donald E. Knuth,et al.  The art of computer programming. Vol.2: Seminumerical algorithms , 1981 .

[9]  Zoran Kapelan,et al.  Dealing with Uncertainty in Water Distribution System Models: A Framework for Real-Time Modeling and Data Assimilation , 2014 .

[10]  Joby Boxall,et al.  Online modelling of water distribution systems: a UK case study , 2009 .

[11]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[12]  Ashu Jain,et al.  Short‐term water demand forecast modeling techniques—CONVENTIONAL METHODS VERSUS AI , 2002 .

[13]  Avi Ostfeld,et al.  On-line hydraulic state prediction for water distribution systems , 2009 .

[14]  Avi Ostfeld,et al.  Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks , 2011 .

[15]  Sarah L. Dance,et al.  Data assimilation for state and parameter estimation: application to morphodynamic modelling , 2013 .

[16]  Donald E. Knuth.  The Art of Computer Programming 2 / Seminumerical Algorithms , 1971 .

[17]  Dominic L. Boccelli,et al.  Real Time Water Demand Estimation in Water Distribution System , 2008 .