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Household water demand estimation and its implications for water accessibility: a case study | ||
| Environmental Resources Research | ||
| دوره 11، شماره 1، مهر 2023، صفحه 131-140 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2023.21641.1410 | ||
| نویسندگان | ||
| Timothy Ogunbode* 1؛ Oladotun M. Ogunlaran2؛ Victor O. Oyebamiji3؛ John A. Akande4 | ||
| 1Assistant Professor, Environmental Management and Crop Production Unit, College of Agriculture, Engineering and Science, Bowen University, Iwo, Nigeria | ||
| 2Associate Professor, Mathematics Programme, College of Agriculture, Engineering and Science, Bowen University, Iwo, Nigeria | ||
| 3Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria | ||
| 4Professor, Environmental Management and Crop Production Unit, College of Agriculture, Engineering and Science, Bowen University, Iwo, Nigeria | ||
| چکیده | ||
| Spatio-temporal accessibility to water, especially, for household use is expedient to a healthy living. This work attempted the development of predictive models using multiple regression to forecast household water use and also assess the significance of water demand forecasting to water use efficiency and its accessibility in homes. Data used for the study were generated through questionnaire administration and these were analyzed using descriptive and inferential statistics. Domestic water uses consist of 10 defined components (Drinking, Cooking, Bathing, Washing, Cleaning, Car wash, Lawn watering, Coolant/Chiller, Incidental and Livestock) proportionately distributed within Coolant/Chiller and Washing regime and ranged from 0.12% to 38.53% respectively among the regime components. Results of the regression analyses revealed that two home water use components namely Washing and Car wash predict home water use at 95% level of confidence with R2 of 0.872 and the Standard Error of 28.91. With this result, Model 3 showed better accuracy than Models 1 and 2 comparatively. The Incidental use component was not significant and may be ignored in the computation. Two predictive components namely Washing and Car wash generally explain excessive use of water in homes and must be considered to enhance the efficient use and unrestricted accessibility of this vital resource. | ||
| کلیدواژهها | ||
| Household water use؛ water accessibility؛ water use efficiency؛ regression model؛ water demand forecast | ||
| مراجع | ||
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Adamowski, J., and Karapataki, C. 2010. Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. Journal of Hydrologic Engineering 15(10), 729–743.
Ayanshola, A.M., Sule, B.F., and Salami, A.W. 2010. Modelling of residential water demand at household level in Ilorin, Nigeria. Journal of Research Information in Civil Engineering. 7(1).
Babić, B., Dukić, A., and Stanić, M. 2014. Managing water pressure for water savings in developing countries. Water SA. 40(2), 221-232.
Crouch, M.L., Jacobs, H.E., and Speight, V.L. 2021. Defining domestic water consumption based on personal water use activities. Journal of Water Supply Research and Technology—AQUA. 70(7), 1002-1011.
Eslamian, S.A., Li, S.S., and Haghighat, F. 2016. A New multiple regression model for prediction of urban water use. Sustainable Cities and Society, 27:419-429. https://doi.org /10.1016/j.scs-2016-08-003.
Fan, L., Liu G., Wang, F., Geissen, V., and Ritsema, C.J. 2013. Factors Affecting Domestic Water Consumption in Rural Households upon Access to Improved Water Supply: Insights from the Wei River Basin, China. PLoS ONE. 8(8), e71977.
Fontanazza, C.M., Notaro, V., Puleo, V., and Freni, G. 2014. Multivariate statistical analysis for water demand modelling. Procedia Engineering. 89(2014), 901-908.
Glecick, P.H., Smith, J.C., and Cooley, H. 2011. Water use efficiency and productivity: Rethinking the basin approach. Water International. 36(7), 784-798.
Gustavo, B.M., Carlos, R.M., and Yilsy, M.N.G. 2009. Water Consumption Range Prediction in Huelva’s Households Using Classification and Regression Trees. Water. 13, https://doi.org/10.3390/w13040506.
Haque, M.M., Rahman, A., Hagare, D., and Kibria, G. 2013. Principal Component Regression Analysis in Water Demand forecasting: An Application to the Blue Mountains, NSWAustralia (Technical Paper). Journal of Hydrology and Environmental Research.1(1),49-59.
Ifabiyi, I.P., and Ahmed, Y.A. 2011. Determinants of household water demand in a traditional city: examples from the western axis of Ilorin, Nigeria. Asian-African Journal of Economics and Econometrics. 11(2), 395-408.
Jeon, J. 2015. The strength and limitations of the statistical modelling of complex social phenomenon: Focusing on SEM, Path Analysis, or Multiple Regression Models. International J. Economics and Management Engineering, 9(5), 9.
Jumin, E., Zaini, N., Najah, A.M., and Abdullah, S. 2020. Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction. Engineering Applications of Computational Fluid Mechanics. 14(1), 713-725.
Karamouz, M., Zahmatkesh, Z., and Nazif, S. 2011. Selecting a domestic water demand prediction model for climate change studies. World Environmental and Water Resources Congress, 22-26 May 2011 held at Palm Springs, California, USA.
Lee, D., and Derrible, S. 2020. Predicting residential water demand with machine-based statistical Learning. Journal Water Resources Planning and Management. 146(1), 04019067.
Liu, Z., and Xue, L. 2017. Forecast of Water Demand in Beijing in 2030. AIP Conference Proceedings1864, 020125.
Mbaya, L.A. 2008. Analysis of water consumption pattern among residential areas in Gombe metropolis. Continental Journal of Applied Sciences. 3, 77-84.
Mudashiru, R.B., Olawuyi, M.Y., Amototo, I.O., Oyelakin, M. A., Adeyemi A. O., and Adekeye A. W. 2021. Evaluation of Household Water Uses Pattern and Determinants using Multiple Regression Models. International Journal of Engineering, Research and Technology. 14(5), 410-418.
Munbi, A.W., Li, F., Bavumiragira, J.P., and Fangninou, F.F. 2002. Forecasting water consumption on transboundary water resource management using fee-forward neural network: A case study of the Nile River in Egypt and Kenya. Marine and Freshwater Research. 73, 292-306.
Nauges, C., and Whittington, D. 2010. Estimation of Water Demand in Developing countries: An Overview. World Bank Research Observer. 25(2), 263-294.
Nnametu, J.N., Alaka, I.N., and Okoronkwo, C.D. 2015. Staff Housing: Panacea to Academic Productivity (Nigerian Institutions). Paper presented at the 2nd Annual Conference of European Real Estate Society, held in Istanbul, Turkey in January. https://doi.org/10.15396 /ere2015-26.
Olawunni, A.O., Akinjare, O.A., and Izobo-Martins, O.O. 2012. User’s satisfaction with residential facilities in Nigerian Private Universities: A Study of Covenant University. Transnational Journal of Science and Technology. 2(11), 89-112.
Ogunbode, T. O., and Ifabiyi, P.I. 2014. Determinants of domestic water consumption in a growing urban centre in Osun State, Nigeria. African Journal of Environmental Science and Technology. 8(4), 247-255.
Ogunbode, T.O. 2015. Pattern of Domestic Water Utilization and Management in Selected Rural Areas of Oyo State, Nigeria. An Unpublished PhD Thesis submitted to the Department of Geography, University of Ilorin, Nigeria, p288.
Ogunbode, T.O., and Ifabiyi, I.P. 2017. Domestic Water Utilization and Its Determinants in the Rural Areas of Oyo State, Nigeria Using Multivariate Analysis. Asian Research Journal of Arts and Social Sciences. 3(3),1-13.
Pena-Guzman, C., Melgarejo, J., and Prats, D. 2016. Forecasting Water Demand in Residential, Commercial and Industrial Zones in Bogota, Colombia Using Least-Squares Support Vector Machine. Mathematical Problems in Engineering.
Qiao, Z., Wu, L., and Yang, Z. 2022. Prediction of Water consumption in 31 Provinces of China Based on FGM (1,1) Model. Clean Soil, Air and Water. https://doi.org/10.1002/clean.202200052.
Rahim, M.S., Nguyen, K.A., Stewart, R.A., Giurco, D., and Blumenstein, M. 2019. Predicting Household Water Consumption Events: Towards a Personalized recommended System to Encourage Water-conscious Behaviour. International Joint Conference on Neural Networks. pp. 1-8
Reynaud, A. 2015. Modelling Household Water Demand in Europe - Insights from a Cross-Country Econometric Analysis of EU-28 countries.EUR 27310. Luxembourg: Publications Office of the European Union.
Shaban A., and Sharma, R.N. 2007. Water Consumption Patterns in Domestic Households in Major Cities Economic and Political Weekly. 42(23), 2190–2197.
Wang, Z., Wu, X., Wang, H., and Wu, T. 2021. Prediction and analysis of domestic water consumption based on optimized grey and Markov Model. Water Supply. 21(7), 3887-3899.
Wafula, P.N., and Ngigi, T.G. 2015. GIS-Based Analysis of Supply and Forecasting piped water demand in Nairobi. International Journal Engineering Science Invention. 4(2),1-11. | ||
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