Investigating the Effective Factors of Leaky Aquifers Using a Novel Approach
Seyed Kazem Sadat Shokouhi, Azam Dolatshah, Hamidreza Vosoughifar, Yousef Rahnavard
Abstract
Seyed Kazem Sadat Shokouhi, Azam Dolatshah, Hamidreza Vosoughifar, Yousef Rahnavard
Abstract
Hydrogeological parameters of leaky aquifers are very important in site characterization, so these parameters are necessary information for quantitative and qualitative groundwater studies. In this paper, different methods were used to evaluate and predict the effective factors of leaky aquifer including the storage and transmissivity coefficients and hydraulic conductivity for four aquifer types classified as A, B, C and D. Pumping test was considered to calculate the storage and transmissivity coefficients experimentally and numerically. Also, Constant Head test, Single Well test, Double Well test, and Hazen formula were considered to obtain the hydraulic conductivity. Furthermore, several feed-forward two-layer artificial neural networks were trained to predict the storage coefficient, transmissivity, and hydraulic conductivity using these tests, models, or formulas. Finally, the sensitivity analysis of all these hydrogeological parameters against the change of each parameter such as discharge and the size of the gravel of the soil was done. Results indicated the behavior of the hydrogeological parameters against each parameter.
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Hydrogeological parameters of leaky aquifers are very important in site characterization, so these parameters are necessary information for quantitative and qualitative groundwater studies. In this paper, different methods were used to evaluate and predict the effective factors of leaky aquifer including the storage and transmissivity coefficients and hydraulic conductivity for four aquifer types classified as A, B, C and D. Pumping test was considered to calculate the storage and transmissivity coefficients experimentally and numerically. Also, Constant Head test, Single Well test, Double Well test, and Hazen formula were considered to obtain the hydraulic conductivity. Furthermore, several feed-forward two-layer artificial neural networks were trained to predict the storage coefficient, transmissivity, and hydraulic conductivity using these tests, models, or formulas. Finally, the sensitivity analysis of all these hydrogeological parameters against the change of each parameter such as discharge and the size of the gravel of the soil was done. Results indicated the behavior of the hydrogeological parameters against each parameter.
Key concepts: Hydrogeology, Aquifer, Hydraulic conductivity, Specific storage, Soil science, Hydraulic head, Geotechnical engineering, Groundwater