PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 23 | 4 |
Tytuł artykułu

An improved coupling model of grey-system and multivariate linear regression for water consumption forecasting

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Water prediction is the basis for water resource planning and management. However, water resource systems are complex. Water consumption is influenced by various factors whose relations are also complicated. The degree of influence is always different for the same factor in different areas. The effective factors of water consumption are analyzed thoroughly. The influencing factors of high degree are selected to establish an improved coupling model of grey system and multiple regressions to predict water consumption in Wuhan. The coupling model is clear in concept, simple in structure, and convenient in use. The complex relationship between water consumption and its main influencing factors is reflected. The model has the potential advantage for predicting annual water consumption. The applied research in Wuhan showed that the forecast effect of improved coupled model is good with relative error less than 1%. The model is used to predict water consumption of 2015 in Wuhan as 4.1430424 billion tons.
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
23
Numer
4
Opis fizyczny
p.1165-1174,ref.
Twórcy
autor
  • College of Urban Construction, Wuhan University of Science and Technology, Wuhan, China
  • School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
autor
  • School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Bibliografia
  • 1. ARBUES F., GARCIA-VALINAS M. A., MARTINEZ- ESPINEIRA R. Estimation of residential water demand: a state-of-the-art review. Journal of Socio-Economics, 32, (1), 81, 2003.
  • 2. FOX C., MCINTOSH B. S., JEFFREY P. Classifying households for water demand forecasting using physical property characteristics. Land Use Policy, 26, (3), 558, 2009.
  • 3. ALI YURDUSEV M., FIRAT M. Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey. J. Hydrol., 365, (3), 225, 2009.
  • 4. LOWRY J. H. JR., RAMSEY R. D., KJELGREN R. K. Predicting urban forest growth and its impact on residential landscape water demand in a semiarid urban environment. Urban Forestry & Urban Greening, 10, (3), 193, 2011.
  • 5. ALMUTAZ I., AJBAR A., KHALID Y., ALI E. A proba­bilistic forecast of water demand for a tourist and desalina­tion dependent city: Case of Mecca, Saudi Arabia. Desalination, 294, (1), 53, 2012.
  • 6. BENNETT C., STEWART R. A., BEAL C. D. ANN-based residential water end-use demand forecasting model. Expert Syst. Appl., 40, (4), 1014, 2013.
  • 7. BECK L., BERNAUER T. How will combined changes in water demand and climate affect water availability in the Zambezi river basin?. Global Environ. Chang., 21, (3), 1061, 2011.
  • 8. GATO S., JAYASURIYA N., ROBERTS P. Temperature and rainfall thresholds for base use urban water demand model­ling. J. Hydrol., 337, (3), 364, 2007.
  • 9. PULIDO-CALVO I., MONTESINOS P., ROLDAN J., RUIZ-NAVARRO F. Linear regressions and neural approaches towater demand forecasting in irrigation districts with telemetry systems. Biosystems Engineering, 97, (1), 283, 2007.
  • 10. CHEN H., YANG Z. F. Residential water demand model under block rate pricing: A case study of Beijing, China. Communications in Nonlinear Science and Numerical Simulation, 14, (5), 2462, 2009.
  • 11. MOHAMED M., AYSHA A., AL-MUALLA. Water demand forecasting in Umm Al-Quwain using the constant rate model. Desalination, 259, (3), 161, 2010.
  • 12. QI C., CHANG N.-B. System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. J. Environ. Manage., 92, (6), 1628, 2011.
  • 13. FIRAT M., TURAN M. E., ALI YURDUSEV M. Comparative analysis of fuzzy inference systems for water consumption time series prediction. J. Hydrol., 374, (3), 235, 2009.
  • 14. FIRAT M., TURAN M. E., ALI YURDUSEV M. Comparative analysis of neural network techniques for pre­dicting water consumption time series. J. Hydrol., 384, (2), 46, 2010.
  • 15. SUGANTHI L., ANAND A. S. Energy models for demand forecasting - A review. Renew. Sust. Energ. Rev., 16, (2), 1223, 2012.
  • 16. ZHANG G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, (1) , 159, 2003.
  • 17. PULIDO-CALVO I., GUTIERREZ-ESTRADA J. C. Improved irrigation water demand forecasting using a soft- computing hybrid model. Biosystems Engineering., 102, (2) , 202, 2009.
  • 18. WANG X., SUN Y., SONG L., MEI C. An eco-environ- mental water demand based model for optimising water resources using hybrid genetic simulated annealing algo­rithms. Part II. Model application and results. J. Environ. Manage., 90, (8), 2612, 2009.
  • 19. NASSERI M., MOEINI A., TABESH M. Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming. Expert Syst. Appl., 38, (6), 7387, 2011.
  • 20. DENG J. Introduction to grey mathematical resource sci­ence. Huazhong University of Science and Technology Press, Wuhan. 2007.
  • 21. DANG Y. Research of Grey Forecasting and Decision Model. Science Press, Beijing. 2009.
  • 22. Wuhan Statistical Yearbook. Wuhan bureau of statistics. Beijing: China Statistics Press, 2012.9., No. 24, 2012.
  • 23. Wuhan water resources bulletin (2004-2011), Wuhan Water Affairs Bureau.
Uwagi
rekord w opracowaniu
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-7d15b4c2-d2a6-4fe0-8bad-81bbca686256
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.