Abstract
Groundwater level data is an important indicator of the availability and distribution of groundwater resources of the region. However, it is difficult t o understand the continuous and discrete fluctuatio ns of the groundwater level which is controlled by various fa ctors. This study demonstrated the use of Fourier s eries 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.
Highlights
Groundwater is the principal source of drinking water for about 50% of the population in the Unites States (Solley et al, 1998)
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
The Fourier series was integrated with the least square estimation method to model the groundwater level fluctuations
Summary
Groundwater is the principal source of drinking water for about 50% of the population in the Unites States (Solley et al, 1998). The key data which provides information on groundwater distribution and availability is water-level measurement from observation wells. It provides critical information regarding hydrologic stresses acting on aquifers and how these affect groundwater dynamics such as recharge, storage and discharge. Groundwater level fluctuation is continuous in nature but changes frequently to discrete on the interruption of human activity. Precipitation and evaporation have a periodic impact on groundwater level fluctuation whereas manual extractions (for irrigation and others) are rather uncertain Some natural factors such as crustal movement and tide and human factors provide some random changes. There have been several modeling development and application to simulate the changes in groundwater depth under both continuous and discrete conditions. Successful application of the technique will provide a robust tool for groundwater modeling where groundwater level fluctuation is heavily influenced on a seasonal basis
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