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Time series modelling is of fundamental importance in forecasting weather that is basically one of
the most technologically and scientifically challenging problems around the world currently. To
make an accurate prediction is certainly one of the key challenges that meteorologists are facing
all over the world. One of the most affected areas is the rainfall patterns, which is being influenced
by global warming, causing drastic changes in its patterns that are characterized by either very high
or low precipitation and temperature. These extreme changes have been identified as major global
challenges of recent times. Meteorological scientist always tries to find means to understand the
atmosphere of the Earth, and to develop accurate weather prediction models. Several methods have
been used in weather prediction, which includes, Classical vector Autoregressive (VAR) models
which perform only polynomial-time computation to compute the probability of the next fixed
model parameters. While this is attractive, it means they cannot model distributions with a time
varying data. They also have a problem with the curse of dimensionality. Recently, machine
learning methods are assumed to be accurate techniques and have been widely used as an
alternative to classical methods for weather prediction. With all these powerfulness and popularity
machine learning methods are not perfect. They have several limitations where they require;
massive datasets, enough time and resources, does not work well with high dimensional data and
have high error vulnerability among others. Despite the availability of different models that are
used by the meteorologists and other departments to make predictions, the same devastating
scenarios of unpredictable weather changes are still being experienced. Therefore, robust models
reliable for accurate predictions are needed on short- and long-term time scales to reduce potential
risks and damages that may occur due to unexpected weather changes. These short comings are
well addressed by the Bayesian Vector Autoregressive (BVAR) models. The purpose of this study
was to develop a BVAR model for predicting rainfall patterns in Kenya. The specific objectives
were to; perform diagnostic analysis of the weather variables; develop Bayesian Vector
Autoregressive predictive model; conduct model performance analysis and apply the model to
forecast the rainfall patterns in Kenya. The Augmented Dicker Fuller and Granger Causality tests
were used for diagnostic analysis. The research adopted secondary data for a period of four years
(2014-2018), which was sourced from Trans-African Hydro-Meteorological Observatory
(TAHMO) and Kenya Meteorological stations. Bayesian Vector Autoregressive model was
developed using multiple regression analysis in a system of equations. The model imposes
structures through information prior beliefs on the parameters which were obtained from VAR
models, likelihood models between the true parameters and the measured variables and the
posterior distribution which is the conditional distribution of the parameter given the
measurements. The model sensitivity was performed using the confusion matrix. The F-test was
used to compare the variances of the actual and the predicted rainfall values. The data was analyzed
using R-Statistical Software. The study found that; the data variables were stationary after at least
the first differencing. Temperature, atmospheric pressure, wind speed and relative humidity were
statistically significant (p < 0.05) determinants of rainfall in all five zones, while wind gust and
radiation were significant in two zones, coast and arid areas. The BVAR model developed was
statistically significant (R 2 = 0.9896). The performance of the model was adequate (RMSE=
86.81%) and its sensitivity was 82.52%, making it appropriate for forecasting. There was no
significant difference between the variances of the actual and predicted values of rainfall (p =
0.3893) at the 5% level of significance in zone five. The study made the following conclusion,
after at least the one differencing the weather variables were found to be stable, the developed
model coefficients were found to be statistically significant, the model performance was good and
it forecasting ability was termed as high. In conclusion, the Bayesian Vector Autoregressive model
developed is suitable for forecasting rainfall patterns in Kenya. The study recommends adoption
of the BVAR model by relevant authorities to predict rainfall. For further research the study
recommends for use of more weather variables to make more accurate prediction. The study also
recommends for development of dynamic weather model, which tests for the impulse response of
weather variable in respect to change in other endogenous variables. |
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