| dc.description.abstract | Rainfall is of critical importance for many people, particularly those whose livelihoods depend on rainfed agriculture. Predicting the trend of rainfall is a difficult task, and statistical approaches such as time
series analysis provide a means for predicting the patterns of rainfall. The models also offer the potential
to improve areas such as increased food production, profitability, and improved food security policing.
However, these forecasts and information systems may, in some instances, not be suitable for direct use
by stakeholders in their decision-making. The objective of this study was to investigate rainfall variability
and develop a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the
monthly rainfall using time series data. Secondary monthly data from 1998 to 2017 for Embu County was
collected from the Kenya Meteorological Department, Embu and recorded into an excel sheet. R-software
was utilized to analyse data for descriptive statistics, rainfall variability, and model fitting. The coefficient
of variation for annual and seasonal rainfall was calculated. The Box Jenkin's ARIMA modelling
procedure (model identification, model estimation, model validation) was used to determine the best
models for the data. The main study findings indicated the existence of annual variability of 34%, March-April-May rainfall variability of 44%, and October-November-December variability of 44%. A first-order
differenced SARIMA (1, 1, 1) (0, 1, 2)12 model with an AIC score of 9.99356 was found suitable for
predicting rainfall pattern in Embu, County. The study outcome revealed that Embu County experiences
high seasonal and rainfall variation of rainfall, thus requires a reliable model for better prediction. | en_US |
