Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2022 / Water is a precious natural resource and one of the most vital substance
for sustainability of life . The increase in water evaporation is a major prob lem where factors such as high temperature and minimum rainfall are the
contributing factors. The aim of the study was to perform time series mod elling of water evaporation from the selected dams in the Limpopo province
South Africa. A daily evaporation time series data was used in the study
with variables such as temperature and rainfall. Daily water evaporation
rate time series data was differenced to make the data series stationary and
Dickey-Fuller test was used to test the stationarity of the data series. The
Autoregressive Conditional Heteroskasticity (ARCH) and Generalized Au toregressive Conditional Heteroskasticity (GARCH) model was performed
on the water evaporation time series data from the selected dams. Vec tor Autoregression (VAR) was used to determine the relationship between
the variables evaporation, rainfall and temperature. Identification of time
series models was done using the autoregressive integrated moving average
(ARIMA). The best ARIMA models were selected based on the autocor relation function (ACF) and partial autocorrelation function (PACF), and
the smallest value of Bayseian Information (BIC). The best models selected
for each dam are: Mokolo dam, ARIMA (1, 1, 2) model; Ga-Rantho dam,
ARIMA (1, 1, 2) model; Leeukraal DeHoop dam, ARIMA (1, 1, 1) model
and Luphephe dam, ARIMA (2, 1, 3) model. The correlation coefficient,
coefficient of determinant (R2
) and root mean square (RMSE) were used to
determine the performance of the model. The water evaporation time series
data from the selected dams was forecasted using the best selected ARIMA
models from the selected dams and then predicted for the next 3 years, where
the results showed a positive constant water evaporation rate.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3902 |
| Date | January 2022 |
| Creators | Phasha, Mmanyaku Goitsemang |
| Contributors | Maposa, D. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xix, 125 leaves |
| Relation |
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