Quantification of Water Savings due to Drought Restrictions in Water Demand Forecasting Models

AbstractThis paper presents a technique to quantify water savings due to implementation of water restrictions by adopting water restriction indexes as a continuous numerical predictor variable in regression analysis. The adopted modeling technique compares four methods: yearly base difference method, weighted average method, before and after method, and expected use method. These methods are applied to single and multiple dwelling residential sectors in the Blue Mountains region, Australia. In the study, three forms of multiple regression techniques are adopted: raw data, semi-log, and log-log. The model performances are evaluated by a number of statistics such as relative error, Nash-Sutcliffe coefficient, and percentage bias. Moreover, the potential of using the water restriction savings and water conservation savings as continuous predictor variables in the water demand forecasting model is investigated. The performances of different modeling techniques are evaluated using split-sample and leave-one-ou...

[1]  Rodney Anthony Stewart,et al.  Identifying Residential Water End Uses Underpinning Peak Day and Peak Hour Demand , 2014 .

[2]  T. Mazzuchi,et al.  Urban Water Demand Forecasting: Review of Methods and Models , 2014 .

[3]  David E. Rosenberg,et al.  Estimating and Verifying United States Households' Potential to Conserve Water , 2012 .

[4]  Vasilis Sarafidis,et al.  An Econometric Assessment of Pricing Sydney’s Residential Water Use , 2012 .

[5]  B. Dziegielewski,et al.  Scenario-Based Forecast of Regional Water Demands in Northeastern Illinois , 2012 .

[6]  Luisa Fernanda Ribeiro Reis,et al.  Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series , 2011 .

[7]  M. Burton,et al.  Urban water restrictions: Attitudes and avoidance , 2011 .

[8]  Richard N. Palmer,et al.  Evaluating Water Demands under Climate Change and Transitions in the Urban Environment , 2011 .

[9]  F. Spaninks.  Estimating the savings from water restrictions in Sydney , 2010 .

[10]  Neville D. Crossman,et al.  The value of public and private green spaces under water restrictions. , 2010 .

[11]  Jefferson S. Wong,et al.  Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast , 2010 .

[12]  Heejun Chang,et al.  Spatial Analysis of Water Use in Oregon, USA, 1985–2005 , 2009 .

[13]  J. Adamowski.  Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks , 2008 .

[14]  David Zimbra,et al.  Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model , 2008 .

[15]  Jessica L. Lowrey,et al.  Residential Water Demand Management: Lessons from Aurora, Colorado 1 , 2008 .

[16]  Sorada Tapsuwan,et al.  The Welfare Costs of Urban Outdoor Water Restrictions , 2007 .

[17]  Niranjali Jayasuriya,et al.  Forecasting Residential Water Demand: Case Study , 2007 .

[18]  M. Babel,et al.  A multivariate econometric approach for domestic water demand modeling: An application to Kathmandu, Nepal , 2007 .

[19]  Helen Higgs,et al.  Urban Water Demand with Fixed Volumetric Charging in a Large Municipality: The Case of Brisbane, Australia , 2006 .

[20]  Anna Montini,et al.  The Determinants of Residential Water Demand Empirical Evidence for a Panel of Italian Municipalities , 2005 .

[21]  Martyn P. Clark,et al.  USE AND EFFECTIVENESS OF MUNICIPAL WATER RESTRICTIONS DURING DROUGHT IN COLORADO 1 , 2004 .

[22]  Mohammed Lu’ay Jamal Froukh,et al.  Decision-Support System for Domestic Water Demand Forecasting and Management , 2001 .

[23]  Ashu Jain,et al.  Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks , 2001 .

[24]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[25]  Richard D. Green,et al.  Do Residential Water Demand Side Management Policies Measure Up? An Analysis of Eight California Water Agencies , 2000 .

[26]  J. McGuckin,et al.  NONPRICE WATER CONSERVATION PROGRAMS AS A DEMAND MANAGEMENT TOOL 1 , 1999 .

[27]  Soroosh Sorooshian,et al.  Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration , 1999 .

[28]  Uri Shamir,et al.  Forecasting Hourly Water Demands by Pattern Recognition Approach , 1993 .

[29]  Douglas T. Shaw,et al.  Urban Drought Response in Southern California: 1990–91 , 1992 .

[30]  David R. Maidment,et al.  Effects of conservation on daily water use , 1988 .

[31]  J. Moncur.  Urban water pricing and drought management , 1987 .

[32]  Raymond L. Anderson,et al.  WATER SAVINGS FROM LAWN WATERING RESTRICTIONS DURING A DROUGHT YEAR, FORT COLLINS, COLORADO , 1980 .

[33]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[34]  M. Haque,et al.  Principal component regression analysis in water demand forecasting : an application to the Blue Mountains, NSW, Australia , 2013 .

[35]  Jay R. Lund,et al.  Residential water conservation in Australia and California. , 2013 .

[36]  Ali Azadeh,et al.  Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City , 2012 .

[37]  Richard N. Palmer,et al.  Seasonal Residential Water Demand Forecasting for Census Tracts , 2010 .

[38]  Kerry A. Fair,et al.  Analysis of water savings: a case study during the 2004/05 water restrictions in Cape Town , 2007 .

[39]  Jammalamadaka.  Introduction to Linear Regression Analysis (3rd ed.) , 2003 .

[40]  M. Renwick,et al.  Demand Side Management Policies for Residential Water Use: Who Bears the Conservation Burden? , 1998 .