Time series analysis of water consumption in the Hohoe municipality of the Volta region, Ghana

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Water is considered as a lifeline of all living things, especially humans; hence its availability is a critical component in the measurement of human wellbeing through the Human Development Index (HDI). Its production and distribution in Ghana, particularly in the Hohoe Municipality of the Volta Region is a challenge. This study seeks to identify the best-fit time series model to the water consumption data in the Hohoe Municipality and to forecast water consumption in the Municipality. This underpins the development of a time-series model for forecasting water consumption levels of the residents, institutions and businesses in the municipality. Several time series models, including AR, MA, ARMA, ARIMA and SARIMA were fitted to the data, and it emerged that the most adequate model for the data was ARIMA (2, 1, and 2). The model was then used to forecast the consumption for the next four years, to advise Ghana Water Company Limited in the municipality to meet the demand of the people.


  • Keywords


    Human Development; Municipality; ARIMA Model; Forecasting; Consumption.

  • References


      [1] Bougadis, J., Adamowski, K., Diduch, R. (2005). “Short-term municipal water demand forecasting.” Hydrological Processes 19, 137–148.http://dx.doi.org/10.1002/hyp.5763.

      [2] Gato, S., Jayasuriya, N., Roberts, P. (2007). “Temperature and rainfall thresholds for base use urban water demand modelling.” Journal of Hydrology 337 (3–4), 364– 376.http://dx.doi.org/10.1016/j.jhydrol.2007.02.014.

      [3] Hagan, I. (2007). Modeling the impact of small reservoirs in the Upper East Region of Ghana.MSc thesis submitted to Lund University Press, Sweden.

      [4] Herrera, M., Torgo, L., Izquierdo, J., and Perez-Garcia, R. (2010). “Predictive models for forecasting hourly urban water demand.” Journal of Hydrology 397, 141-150.http://dx.doi.org/10.1016/j.jhydrol.2010.04.005.

      [5] Jain, A., Varshney, K., Joshi, U. (2001). “Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks.” Water ResourcesManagement 15, 299–321.http://dx.doi.org/10.1023/A:1014415503476.

      [6] Khan, M., Coulibaly, P. (2006). “Application of support vector machine in Lake water level prediction.” Journal of Hydrological Engineering 11 (3), 199–205.http://dx.doi.org/10.1061/(ASCE)1084-0699(2006)11:3(199).

      [7] Lertpalangsunti, N., Chan, C., Mason, R., Tontiwachwuthikul, P. (1999). “A tool set for construction of hybrid intelligent forecasting systems: application for water demand prediction.” Artificial Intelligence in Engineering 13, 21–42.http://dx.doi.org/10.1016/S0954-1810(98)00008-9.

      [8] Maidment, D.R., Miaou, S.P. Crawford (1986). “Daily water use in nine cities.” Water Resources Research 22 (6), 845–851.http://dx.doi.org/10.1029/WR022i006p00845.

      [9] Maidment, D.R., Miaou, S.P., Crawford, M.M. (1985). “Transfer function models of daily urban water use.” Water Resources Research 21 (4), 425–432.http://dx.doi.org/10.1029/WR021i004p00425.

      [10] Salas-LaCruz, J. D. and Yevjevich, V. (1972). Stochastic Structure of Water Use Time Series. Hydrology Paper 52. Colorado State University, Fort Collins, Colorado. 71p.

      [11] World Health Organisation [WHO] (1997). Guidelines for drinking-water quality: Surveillance and control of community supplies. Geneva: WHO.

      [12] Zhou, S.L., McMahon, T.A., Walton, A., Lewis, J. (2002). “Forecasting operational demand for an urban water supply zone.” Journal of Hydrology 259, 189-202.http://dx.doi.org/10.1016/S0022-1694(01)00582-0.


 

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Article ID: 3629
 
DOI: 10.14419/ijamr.v4i2.3629




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