PREDICTING GROUNDWATER LEVEL USING FOURIER SERIES INTEGRATED WITH LEAST SQUARE ESTIMATION METHOD

Abstract

Groundwater level data is an important indicator of the availability and distribution of groundwater resources of the region. However, it is difficult to understand the continuous and discrete fluctuations of the groundwater level which is controlled by various factors. This study demonstrated the use of Fourier series integrated with the least square estimation method to predict the groundwater level especially in the case of seasonal-sensitive groundwater fluctuations. It was observed that the designed method was able to model the groundwater-table data, collected at the Hagan Stone Park station in Greensboro, North Carolina, with a fair degree of accuracy with a testing mean square error of 0.0735.