Water demand time series generation for distribution network modeling and water demand forecasting

Abstract Knowledge of water consumption can help water distribution network modeling. Consumption has been studied based on its stochastic and spatial random features. Considering the difficulty to have access to real-demand data, this work presents three methods to generate synthetically water demand time series from observed data, thus producing final demands in different ways. Each method generates a time series that considers either temporal consumption trends or jointly temporal trends and climatic influence. A random forest algorithm is applied to obtain the relevance of each climatic variable. This study uses water demand and climatic data of various Brazilian cities to extract temporal patterns. The final synthetically generated data can be used as input data for water network models, to feed the methods used according to the objectives of each study or project.

[1]  Stefano Alvisi,et al.  Generation of synthetic water demand time series at different temporal and spatial aggregation levels , 2014 .

[2]  J. C. van Dijk,et al.  Simulating Nonresidential Water Demand with a Stochastic End-Use Model , 2013 .

[3]  Jan Adamowski,et al.  Using extreme learning machines for short-term urban water demand forecasting , 2017 .

[4]  M. Moglia,et al.  Estimating the effect of climate on water demand: Towards strategic policy analysis , 2009 .

[5]  S. Buchberger,et al.  Model for Instantaneous Residential Water Demands , 1995 .

[6]  Muhammad A. Al-Zahrani,et al.  Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models , 2015, Water Resources Management.

[7]  Stefano Alvisi,et al.  A short-term, pattern-based model for water-demand forecasting , 2006 .

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Joaquín Izquierdo,et al.  Hybrid regression model for near real-time urban water demand forecasting , 2017, J. Comput. Appl. Math..

[10]  I. Vertommen,et al.  Generating Water Demand Scenarios Using Scaling Laws , 2014 .

[11]  Velitchko G. Tzatchkov,et al.  Modeling of Drinking Water Distribution Networks Using Stochastic Demand , 2012, Water Resources Management.

[12]  S. Alvisi,et al.  A Stochastic Model for Representing Drinking Water Demand at Residential Level , 2003 .

[13]  Jan Adamowski,et al.  An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada , 2017 .

[14]  Ni-Bin Chang,et al.  System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. , 2011, Journal of environmental management.