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Journal articles on the topic 'Demand forecasting in Nigeria'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Ayoade, J. O. "Forecasting and managing the demand for water in Nigeria." International Journal of Water Resources Development 3, no. 4 (December 1987): 222–27. http://dx.doi.org/10.1080/07900628708722353.

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2

Olaniyi, Adeniran, Adetayo, and Kanyio, Olufunto Adedotun. "Long Term Forecasting of International Air Travel Demand in Nigeria (2018-2028)." American International Journal of Multidisciplinary Scientific Research 1, no. 2 (September 10, 2018): 16–24. http://dx.doi.org/10.46281/aijmsr.v1i2.184.

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This study examines long term forecasting of international air travel demand in Nigeria. Yearly data from 2001 to 2017 were collected from secondary sources. Ordinary Least Square (OLS) regression was used to forecast the ten years (2018 to 2028) demand for international air passenger travel in Nigeria. The demand for international air passenger in Nigeria from year 2001 to 2017 was compared with the forecast. Calculation reveals that the coefficient of determination R2 is 0.815, while the computed reveals that the coefficient of determination R2 is 0.769, this difference can be attributed to approximations to two decimal places for calculated test. The calculated test and computed test reveals that the error term is minimal and the explanation level is high; hence the prediction or forecast is reliable. The forecast for years 2020, 2025 and 2028 are 5,282,453, 6,342,519, and 6,978,559 respectively which are about 48 percent increase, 78 percent increase, and 95 percent increase respectively from demand in year 2017. The forecast of ten years from year 2018 to year 2028 reveals that there will be more increase in the demand for international air passenger travel in Nigeria. The implication of this increment is that existing air transport infrastructures should be upgraded, and new infrastructures should be procured and installed; airport and airline operations should be reviewed and strategized such that they will meet the expectations of airline and airport users. Other concerned business stakeholders should use this data to plan and invest as there is high tendency for profit making.

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3

Sobowale, A., and K. S. Adeyemo. "Modeling water demand in a growing public university in Nigeria." Nigerian Journal of Technology 39, no. 4 (March 24, 2021): 1255–62. http://dx.doi.org/10.4314/njt.v39i4.35.

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Nigerian university campuses are facing the twin problems of providing portable water of adequate quantity and quality and the sustainability of such supply. This paper examines the water demand status of a public University in Nigeria. Domestic, Public and Industrial water uses were considered while population forecasting was done using regression analysis for a 30 years design period (2018 – 2048). Results reveals possible population increase of 53.8 % by 2048 when the institution will clock 60 years. Water demand is also expected to rise sharply from 5,206 m3 day -1 (2018) to 10,959 m3 day -1 (2048); existing storage capacity cannot satisfy the current needs not to talk of the projected demand hence, a reservoir of about 11,000 m3 will be needed to service the university for the next 30 years; attracting more investments into the water supply system becomes imperative as the existing supplies from groundwater is unsustainable. Keywords: Water Demand; Population; University; Sustainability; Growth

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4

Oyediran Oyelami, Benjamin, and Adedamola adedoyin Adewumi. "Models for Forecasting the Demand and Supply of Electricity in Nigeria." American Journal of Modeling and Optimization 2, no. 1 (March 8, 2014): 25–33. http://dx.doi.org/10.12691/ajmo-2-1-4.

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5

Ogundari, I. O., A. S. Momodu, A. J. Famurewa, J. B. Akarakiri, and W. O. Siyanbola. "Analysis of Sustainable Cassava Biofuel Production in Nigeria." Energy & Environment 23, no. 4 (June 2012): 599–618. http://dx.doi.org/10.1260/0958-305x.23.4.599.

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Nigeria's biofuels policy advocates the adoption of cassava as feedstock for a 10%-biofuel substitution option in Nigerian transport fuel demand. This policy option is expected to address energy security and environmental consequences of using fossil fuels as the sole source of transport energy in the country. This paper appraised the technological and economic factors necessary for achieving Nigeria's cassava-based biofuel initiative at different substitution levels of 5, 10, and 15% by the Year 2020. A multi-stage energy forecasting and project analysis framework adapted from Coate's structure for technology assessment, as well as engineering economy methodology was used for the study. Technological analysis entailed determining petrol consumption projection, R&D capability, input feedstock requirements, environmental considerations and land requirement for feedstock crop production while engineering economy analysis evaluated the economic viability of the project. The results showed that petrol consumption in Nigeria and bioethanol substitution requirements were in the range of 18,285.7 – 19,142.84 thousand tons and 914.28 (5% low demand) – 2871.43 (15% high demand) thousand tons, respectively by 2020. Cassava feedstock and landmass requirements for bioethanol production were in the range of 4.64 – 14.53 million tons and 4.08 – 12.80 thousand sq. km, respectively while carbon dioxide savings were between 1.87 – 5.89 million tons by 2020. The recovery price for cassava bioethanol was estimated to be US$ 0.74/litre [Formula: see text]. Petrol being subsidised presently is harmful to the environment though it ‘oils’ the economy. Nigeria currently subsidizes petroleum products to the tune of 28% of 2011 budget. The government plans to remove this by 2012. Thus we conclude that weighing both economic and environmental benefits of bioethanol substitution in petrol consumption in Nigeria, the study showed that bioethanol production from cassava feedstock would be both technically and economically viable, provided subsidy, which depends on political will on the side of the government, is introduced for the first ten years of its implementation.

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6

OLUFISAYO, ADE-IKUESAN OLANIKE, ATILOLA MORUFDEEN OLATUNBOSUN, OYEDEJI AJIBOLA OLUWAFEMI, and ADEYEMI HEZEKIAH OLUWOLE. "FUZZY LOGIC APPROACH TO ENERGY PLANNING IN NIGERIA." Journal of Engineering Studies and Research 26, no. 4 (January 8, 2021): 86–96. http://dx.doi.org/10.29081/jesr.v26i4.240.

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Energy planning is an important tool for power system utility company and consumer’s profitability and satisfaction respectively. This paper is a study of energy planning (forecasting) in Ogun state of Nigeria using Fuzzy Logic model. Population and gross domestic product (GDP) are used as the independent variables to forecast load demand based on the previous load demand. After arranging the variables into 5 membership functions and the 19 rules were created, the fuzzy logic model forecast the annual load demand for the next 10 years with a percentage error margin 0.95 % to 21.79 % which results to a mean absolute percentage error (MAPE) of 8.34 %. The result of the forecast shows that within the next 10 years, 2019 to 2028, an average power load of 1985.66 MWH will be required.

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7

Okakwu, I. K., E. S. Oluwasogo, A. E. Ibhaze, and A. L. Imoize. "A comparative study of time series analysis for forecasting energy demand in Nigeria." Nigerian Journal of Technology 38, no. 2 (April 17, 2019): 465. http://dx.doi.org/10.4314/njt.v38i2.24.

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8

Musa, Bashir, Nasser Yimen, Sani Isah Abba, Humphrey Hugh Adun, and Mustafa Dagbasi. "Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach." Processes 9, no. 7 (July 5, 2021): 1166. http://dx.doi.org/10.3390/pr9071166.

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The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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9

Habeeb, Munirat, Umar Mohammed, and Ekechukwu Henry. "EFFECTS OF THE MANAGEMENT OF FINISHED GOODS INVENTORY ON THE SALES VOLUME OF 7UP BOTTLING COMPANY, NORTH CENTRAL NIGERIA." International Journal of Innovative Research in Social Sciences & Strategic Management Techniques 8, no. 1 (January 5, 2021): 70–82. http://dx.doi.org/10.48028/iiprds/ijirsssmt.v8.i1.06.

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The challenge of keeping finished goods inventory at optimum levels has been a major issue that has affected the sales volume of 7UP Bottling Company Plc. This study examined the effects of the management of finished goods inventory and the sales volume of 7UP Bottling Company in North Central, Nigeria. The study examined how forecasting demand, product handling as well product planning and warehouse management impact on the sales volume of7UP Bottling Company in North-Central Nigeria. The study adopted a combination of survey, explanatory and exploratory research, which involved the use of primary data for analysis. The data collection exercise involved a focus group discussion (FGD) with different targeted group of customers. The total population of study was eight one (81) management staff of Seven-Up Bottling Company Plc in North Central, Nigeria and a sample size of sixty seven (67) was drawn using Taro Yamane’s sample size technique.The hypotheses were formulated in null form in line with the objectives of the study and the ordinary Least Squares (O.L.S) method of regression was employed for the analysis of the data collected. Findings revealed that there is a positive significant relationship between management of finished goods inventory and sales volume at (B = 1.896, t = 10.6, Sig = .000, P <.05) in 7UP Bottling Company in North Central, Nigeria. The study therefore recommends that the management of Seven Up Bottling Company Plc should implement the use of queuing systems (i.e. FIFO or LIFO) in the management of its warehouse because the system helps to reduce costs generated as a result of storage of excessive amount of unsold products.

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10

Jones, Rod. "Forecasting demand." British Journal of Healthcare Management 16, no. 8 (August 2010): 392–93. http://dx.doi.org/10.12968/bjhc.2010.16.8.77654.

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11

Elkarmi, Fawwaz, and Nazih Abu Shikhah. "Electricity Demand Forecasting." International Journal of Productivity Management and Assessment Technologies 2, no. 1 (January 2014): 1–19. http://dx.doi.org/10.4018/ijpmat.2014010101.

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Forecasting is the backbone of any planning process in all fields of interest. It has a great impact on future decisions. It is also of great importance to the operation and control of business, which is reflected as profits or losses to the entity. This paper aims to provide the planner with sufficient knowledge and background of the different scopes of forecasting methods, in general, and when applied to power system field, in particular. Various load and energy forecasting models, and theoretical techniques are discussed from different perspectives, time frames, and levels. The paper presents the attributes and importance of forecasting through several cases of research conducted by the author for the Jordanian power system. In all cases the methodologies selected cover short, medium and long term forecasting periods and the results are accurate.

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12

Greenidge, Kevin. "Forecasting tourism demand." Annals of Tourism Research 28, no. 1 (January 2001): 98–112. http://dx.doi.org/10.1016/s0160-7383(00)00010-4.

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13

Bernard Trustrum, Leslie, F. Robert Blore, and William James Paskins. "Using Demand Forecasting Models." Marketing Intelligence & Planning 5, no. 3 (March 1987): 5–15. http://dx.doi.org/10.1108/eb045750.

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14

Guinel, Ipek. "Forecasting system energy demand." Journal of Forecasting 6, no. 2 (1987): 137–56. http://dx.doi.org/10.1002/for.3980060207.

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15

Wong, James M. W., Albert P. C. Chan, and Y. H. Chiang. "Construction manpower demand forecasting." Engineering, Construction and Architectural Management 18, no. 1 (January 11, 2011): 7–29. http://dx.doi.org/10.1108/09699981111098667.

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16

Chambers, Marcus J. "Forecasting with demand systems." Journal of Econometrics 44, no. 3 (June 1990): 363–76. http://dx.doi.org/10.1016/0304-4076(90)90064-z.

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17

Martin, Christine A., and Stephen F. Witt. "Tourism demand forecasting models." Tourism Management 8, no. 3 (September 1987): 233–46. http://dx.doi.org/10.1016/0261-5177(87)90055-0.

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18

Trubchanin, V. V. "Forecasting Product Demand in Terms of Demand Volatility." Bulletin of Ural Federal University. Series Economics and Management 16, no. 2 (2017): 191–207. http://dx.doi.org/10.15826/vestnik.2017.16.2.010.

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19

Strotsen, L. "Qualitative methods of forecasting demand." Galic'kij ekonomičnij visnik 54, no. 1 (2018): 113–18. http://dx.doi.org/10.33108/galicianvisnyk_tntu2018.01.113.

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20

Painter, Kathleen, Eric Jessup, Marcia Hill Gossard, and Ken Casavant. "Demand Forecasting for Rural Transit." Transportation Research Record: Journal of the Transportation Research Board 1997, no. 1 (January 2007): 35–40. http://dx.doi.org/10.3141/1997-05.

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21

Hou, Kai Wen, Kui Ying Wang, Yun Ming Li, Yong He Hu, and Qi Ying Yang. "Emergency Supplies Demand Forecasting Model." Applied Mechanics and Materials 608-609 (October 2014): 129–33. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.129.

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At 8:02 of April 20, 2013, a strong earthquake of magnitude 7.0 occurred in Lushan County, Ya’an, Sichuan, sp that a large area of houses collapsed, and a great loss of people's lives and property was caused. The timeliness and effectiveness of the post-earthquake relief were directly related to saving lives and properties, while the scientific allocation of emergency supplies is a key element in the post-earthquake relief.

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22

Fildes, Robert, and V. Kumar. "Telecommunications demand forecasting—a review." International Journal of Forecasting 18, no. 4 (October 2002): 489–522. http://dx.doi.org/10.1016/s0169-2070(02)00064-x.

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23

Turner, Lindsay. "Tourism demand modelling and forecasting." Tourism Management 22, no. 5 (October 2001): 578–79. http://dx.doi.org/10.1016/s0261-5177(01)00018-8.

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24

Tica, Josip, and Ivan Kožić. "Forecasting Croatian inbound tourism demand." Economic Research-Ekonomska Istraživanja 28, no. 1 (January 2015): 1046–62. http://dx.doi.org/10.1080/1331677x.2015.1100842.

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25

Kalchschmidt, Matteo, Roberto Verganti, and Giulio Zotteri. "Forecasting demand from heterogeneous customers." International Journal of Operations & Production Management 26, no. 6 (June 2006): 619–38. http://dx.doi.org/10.1108/01443570610666975.

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26

Rajopadhye, Mihir, Mounir Ben Ghalia, Paul P. Wang, Timothy Baker, and Craig V. Eister. "Forecasting uncertain hotel room demand." Information Sciences 132, no. 1-4 (February 2001): 1–11. http://dx.doi.org/10.1016/s0020-0255(00)00082-7.

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27

ARAS, HAYDAR, and NIL ARAS. "Forecasting Residential Natural Gas Demand." Energy Sources 26, no. 5 (April 2004): 463–72. http://dx.doi.org/10.1080/00908310490429740.

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28

Böse, Joos-Hendrik, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias Seeger, and Yuyang Wang. "Probabilistic demand forecasting at scale." Proceedings of the VLDB Endowment 10, no. 12 (August 2017): 1694–705. http://dx.doi.org/10.14778/3137765.3137775.

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29

Rostami-Tabar, Bahman, M. Zied Babai, Aris Syntetos, and Yves Ducq. "Demand forecasting by temporal aggregation." Naval Research Logistics (NRL) 60, no. 6 (July 31, 2013): 479–98. http://dx.doi.org/10.1002/nav.21546.

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30

Mirowski, Piotr, Sining Chen, Tin Kam Ho, and Chun-Nam Yu. "Demand Forecasting in Smart Grids." Bell Labs Technical Journal 18, no. 4 (February 26, 2014): 135–58. http://dx.doi.org/10.1002/bltj.21650.

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31

Joyeux, Roselyne, George Milunovich, and John Rigg. "Forecasting Demand for Australian Passports." Asia Pacific Journal of Tourism Research 17, no. 1 (February 2012): 100–119. http://dx.doi.org/10.1080/10941665.2011.613205.

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32

Syntetos, Aris A., Mohamed Zied Babai, and Shuxin Luo. "Forecasting of compound Erlang demand." Journal of the Operational Research Society 66, no. 12 (December 2015): 2061–74. http://dx.doi.org/10.1057/jors.2015.27.

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33

Kolars, J. C. "Forecasting physician supply and demand." Medical Education 35, no. 5 (May 13, 2001): 424–25. http://dx.doi.org/10.1046/j.1365-2923.2001.00945.x.

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34

Vu, Chau Jo, and Lindsay Turner. "Data Disaggregation in Demand Forecasting." Tourism and Hospitality Research 6, no. 1 (November 2005): 38–52. http://dx.doi.org/10.1057/palgrave.thr.6040043.

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It is assumed in tourism demand forecasting that the disaggregation of data is useful in terms of country of origin and also in terms of purpose of travel (Smith and Toms, 1967; Blackwell, 1970; Martin and Witt, 1989a). The primary disaggregation by country is useful for determining regional forecast flows and the disaggregation by purpose of visit has been considered potentially useful for increasing forecasting accuracy given the flows have different characteristics (Turner, Kulendran and Pergat, 1995; Morley and Sutikno, 1991). It is also possible to disaggregate on the basis of age and gender. It has been assumed (because no research has disaggregated by age and gender) in previous research that the gender and age composition of flows is a reflection of the total population and therefore exhibits the same time-series characteristics. This may not be the case, however. This study uses data for tourist arrivals into Korea to test the assumptions that further disaggregation of data on the basis of gender and age is not needed, and to further examine whether disaggregation by purpose of visit is worthwhile, when the purpose is to forecast total country arrivals. Quarterly data from 1994 to 2003 are used with the estimation period 1994–2001 and the post-estimation period 2002–2003. The conclusion from the study is that total arrivals forecasting is not more accurate when the data used is the sum of forecast disaggregated series, as opposed to direct forecasts of total arrivals.

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35

Ferbar Tratar, Liljana. "Forecasting method for noisy demand." International Journal of Production Economics 161 (March 2015): 64–73. http://dx.doi.org/10.1016/j.ijpe.2014.11.019.

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36

Wan, Shui Ki, Haiyan Song, and David Ko. "Density forecasting for tourism demand." Annals of Tourism Research 60 (September 2016): 27–30. http://dx.doi.org/10.1016/j.annals.2016.05.012.

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37

Song, Haiyan, Long Wen, and Chang Liu. "Density tourism demand forecasting revisited." Annals of Tourism Research 75 (March 2019): 379–92. http://dx.doi.org/10.1016/j.annals.2018.12.019.

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38

Chen, Dongling, Kenneth W. Clements, E. John Roberts, and E. Juerg Weber. "Forecasting steel demand in China." Resources Policy 17, no. 3 (September 1991): 196–210. http://dx.doi.org/10.1016/0301-4207(91)90003-e.

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39

Harvey, Edward B., and K. S. R. Murthy. "Forecasting manpower demand and supply." International Journal of Forecasting 4, no. 4 (January 1988): 551–62. http://dx.doi.org/10.1016/0169-2070(88)90132-x.

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40

Yelland, Phillip M. "Bayesian forecasting of parts demand." International Journal of Forecasting 26, no. 2 (April 2010): 374–96. http://dx.doi.org/10.1016/j.ijforecast.2009.11.001.

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41

Long, Wen, Chang Liu, and Haiyan Song. "Pooling in Tourism Demand Forecasting." Journal of Travel Research 58, no. 7 (October 5, 2018): 1161–74. http://dx.doi.org/10.1177/0047287518800390.

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This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

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42

Bonde, Hans, and Hans-Henrik Hvolby. "The demand planning process." Journal on Chain and Network Science 5, no. 2 (December 1, 2005): 73–84. http://dx.doi.org/10.3920/jcns2005.x057.

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In this paper, demand planning is discussed from a process point of view. Demand planning is not only forecasting but goes beyond, as it combines quantitative forecasting with a causal forecasting approach to plan demand by changing factors within pricing, marketing or selling. A four-phase demand planning process model is introduced, which consists of modeling, forecasting, demand planning and supply planning. The core of the process is a demand planning tool, which allows the combination of quantitative, causal and judgmental forecasting. Finally, some thoughts are given on how SMEs can develop their current forecasting practice when implementing demand planning as a strategic and tactical tool.

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43

Xiao, Chuncai, and Yumei Liao. "Transformer Order Demand Forecasting Based on Grey Forecasting Model." IOP Conference Series: Earth and Environmental Science 831, no. 1 (August 1, 2021): 012004. http://dx.doi.org/10.1088/1755-1315/831/1/012004.

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44

Miao, Xin, and Bao Xi. "AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND." TRANSPORT 23, no. 1 (March 31, 2008): 26–30. http://dx.doi.org/10.3846/1648-4142.2008.23.26-30.

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The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.

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45

Rožanec, Jože M., Blaž Kažič, Maja Škrjanc, Blaž Fortuna, and Dunja Mladenić. "Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies." Applied Sciences 11, no. 15 (July 23, 2021): 6787. http://dx.doi.org/10.3390/app11156787.

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Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

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46

Anamika, Anamika, and Niranjan Kumar. "Demand Forecasting in Deregulated Electricity Markets." International Journal of Computer Applications 108, no. 3 (December 18, 2014): 10–15. http://dx.doi.org/10.5120/18889-0171.

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47

Johnston, F. R., and J. E. Boylan. "Forecasting for Items with Intermittent Demand." Journal of the Operational Research Society 47, no. 1 (January 1996): 113. http://dx.doi.org/10.2307/2584256.

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48

Hecht, Robert, and Gian Gandhi. "Demand Forecasting for Preventive??AIDS Vaccines." PharmacoEconomics 26, no. 8 (2008): 679–97. http://dx.doi.org/10.2165/00019053-200826080-00005.

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49

Vasumathi, B., and A. Saradha. "Forecasting Intermittent Demand for Spare Parts." International Journal of Computer Applications 75, no. 11 (August 23, 2013): 12–16. http://dx.doi.org/10.5120/13154-0805.

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50

Fullerton, Thomas J., and Juan P. Cardenas. "Forecasting Water Demand in Phoenix, Ariz." Journal - American Water Works Association 108 (October 1, 2016): E533—E545. http://dx.doi.org/10.5942/jawwa.2016.108.0156.

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