Determination of Aquifer Parameters Using Cloud Neural Network
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Abstract:
Efficient and accurate solutions for determination of aquifer parameters have been one of the most important research topics in hydrogeological research field. The fitting curve method is usually used to determine the aquifer parameters from unsteady pumping test. With the wide computer application, several rapid and accurate computer intelligence optimization algorithms were developed to determine the aquifer parameters under the conditions of unsteady flow. On this basis, the Cloud Neural Net (CNN) model was applied in this paper to calculate the hydraulic parameters of a confined aquifer in Yuanshi County of Shijiazhuang City based on 3 single-hole unsteady flow pumping tests. The model results were in accordance with the actual hydrogeological conditions, and more accurate compared with the results derived from the traditional method and simplified artificial neural net model. Thus CNN model establishes a good foundation for groundwater resources assessment, groundwater numerical simulation, as well as solute transportation simulation.
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