Use of principal components regression and time-series analysis to predict the water level of the Akosombo Dam Level.

Abstract

Knowing the water level of the Akosombo Dam would help Ghanaian since we depend heavily on hydroelectric power. When the future of the water level is known, society would be able to plan on the usage of electricity for the industries, society, individuals who use some of the water storage for irrigation, water supply purposes. The study employed rainfall from the 12 catchment areas to the River Volta and the daily water level of the dam for a period of 78-years. Principal Component Regression was applied to the input variables for the reduction of its large size to a few principal components to explain the variations in the original dataset. The outcome of the PCR extraction was two principal components. Time Series using Seasonal Autoregressive Integrated Moving Average was used to model the data. The appropriate model that fit the data well was ARIMA (2,1,2) (1,0,0) [12] after comparing other models AICs. The model with the smallest AIC and the least number of parameters was selected as the best model.