Municipal water consumption forecast accuracy

被引:12
|
作者
Fullerton, Thomas M., Jr. [1 ]
Molina, Angel L., Jr. [1 ]
机构
[1] Univ Texas El Paso, Dept Econ & Finance, El Paso, TX 79968 USA
关键词
ECONOMIC FORECASTS; DEMAND; PRICE; MODEL; SPECIFICATION;
D O I
10.1029/2009WR008450
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] The usage of Artificial Neural Networks in the Classification and Forecast of Potable Water Consumption
    de Oliveira, Diego Marinho
    de Oliveira Andrade, Andre Luis
    Nobre, Cristiane Neri
    Zarate, Luis Enrique
    [J]. IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 2334 - +
  • [22] Internal (blue) water footprint of municipal consumption: a case study for Turkey
    Boyacioglu, Hulya
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2018, 190 (07)
  • [23] Internal (blue) water footprint of municipal consumption: a case study for Turkey
    Hülya Boyacıoğlu
    [J]. Environmental Monitoring and Assessment, 2018, 190
  • [24] Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling
    Mahmut Firat
    Mehmet Ali Yurdusev
    Mustafa Erkan Turan
    [J]. Water Resources Management, 2009, 23 : 617 - 632
  • [25] Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling
    Firat, Mahmut
    Yurdusev, Mehmet Ali
    Turan, Mustafa Erkan
    [J]. WATER RESOURCES MANAGEMENT, 2009, 23 (04) : 617 - 632
  • [26] RELATIVE FORECAST ACCURACY AND THE TIMING OF EARNINGS FORECAST ANNOUNCEMENTS
    HASSELL, JM
    JENNINGS, RH
    [J]. ACCOUNTING REVIEW, 1986, 61 (01): : 58 - 75
  • [27] Using forecast evaluation to improve the accuracy of the Greenbook forecast
    Arai, Natsuki
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2014, 30 (01) : 12 - 19
  • [28] Short-Run Water Demand Forecast Accuracy for the Tampa Bay Area
    Fullerton, Thomas M., Jr.
    Walke, Adam G.
    Asefa, Tirusew
    [J]. JOURNAL AMERICAN WATER WORKS ASSOCIATION, 2016, 108 (03): : 76 - 76
  • [29] Urban Water Consumption Forecast Based on QPSO-RBF Neural Network
    Zhu, Xingtong
    Xu, Bo
    [J]. PROCEEDINGS OF THE 2012 EIGHTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2012), 2012, : 233 - 236
  • [30] Forecast of annual water consumption in 31 regions of China considering GDP and population
    Meng Xiangmei
    Tu Leping
    Yan Chen
    Wu Lifeng
    [J]. SUSTAINABLE PRODUCTION AND CONSUMPTION, 2021, 27 : 713 - 736