| dc.description.abstract | ABSTRACT
Increased population of Kisumu City over the past years has resulted into high demand
and competition for water and related facilities. This is evident in the persistent water
scarcity within the City, use of poor quality water by the residents and inequitable water
distribution. The current net water supply capacity of Kisumu City Water Supply System
is 5,400m3/day against a demand of 27,000 m3/day. Effective planning and management
of the City's water resources is therefore critical in providing reliable forecasts. Models
developed for such forecasts ought to take into account the non stationary and seasonality
behaviours exhibited by residential water demand data. Research on residential water
demand in the Kenyan context have used Ordinary Least Squares, a methodology that
does not model the seasonality aspect. In the SARIMA(p,d,q)(P,D,Qh2 which is
expressed as ¢p(B)ifJp(Bs)"\ld\ll§xt = eq(B)8Q(BS)et, BS allows for the modelling of
the seasonal behaviour in the data. However, the application of SARIMA to model
and forecast residential water consumption in the Kenyan Context is scanty. The study
therefore sought to propose a SARIMA model for forecasting residential water demand
using secondary monthly water consumption data obtained from KIWASCO for the years
2004 to 2013. Preliminary investigation of the data showed that the data followed a 3-
parameter log-normal distribution. Therefore, using logarithm values of the data, the
study established by both OLS and Kendall's tau test that the residential water demand
for Kisumu City had a significant increasing trend. The KPSS and ADF tests revealed
that the data had unit roots which were however removed by first difference. The Data
was then fitted to a SARIMA model and the parameters of the model were estimated
using Maximum Likelihood Method. SARIMA(l, 1, 1)(0, 1, 1)12 had the least BIC and
AIC values of 2205.273 and 2197.282 respectively and was identified as the better fitting
model. Compared to the OLS model, SARIMA(l, 1, 1)(0, 1, 1h2 had the least MAPE
and RMSE values of 3.59 and 7476.59 respectively implying that it had higher forecasting
performance. One year forecasts for 2014 were established together with their CI. The
observed values for January and February 2014 were within the Confidence Limits. The
study recommends the integration of the model by KIWASCO and other water companies
in their design of water demand management policies. | en_US |
