Spatial estimation of water-table depth by artificial neural networks in light of ancillary data |
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| Authors: | Mehrdad Pasandi Narjes Salmani Nozar Samani |
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| Institution: | 1. Department of Geology, Faculty of Science, University of Isfahan, Isfahan, Iran;2. Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz, Iran |
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| Abstract: | Proper estimation of the spatial distribution of water-table depth is highly important in most groundwater studies. Groundwater depth is measured at specific and limited points and it is estimated for other parts using spatial estimation methods. In this study, two multivariate methods, artificial neural network (ANN) and multiple linear regression (MLR), are examined to estimate water-table depth in an unconfined aquifer located in Shibkooh, Iran. The different ancillary data, including spatial coordinates, digital elevation model (DEM), aquifer bed elevation, specific resistivity and aquifer thickness were used to improve estimates based on these methods. It was proved that performance of the ANN surpasses that of the MLR method. Using the spatial coordinates, the aquifer bed elevation and aquifer thickness resulted in the optimum spatial estimation of the water-table depth. These parameters, directly or indirectly, affect the water-table depth estimation through techniques such as ANN capable of modelling of nonlinear relationships. |
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| Keywords: | D Koutsoyiannis |
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