Knowing of the water table around the region and access to its contour maps is one of the most important planning tools for withdrawal underground aquifers and implementing civil projects. Generally, by using the piezometric wells in the region and different methods of estimation, the water table determined. Limitation of these methods is the inability to estimate water table on different days of the year. In this study, by using artificial neural network and time of the measurements of the water table as one of the inputs, the network is trained to estimate contour maps of water table on different days of the year. For this purpose, the water table data in Behbahan plain for the years 1370 to 1385 were used to training the network. Correlation coefficient 0.9906 between actual values and estimated values of the trained network indicates that the estimation is very good. Finally based on this network, contour map of water table in Behbahan plain is plotted for four different days in 1384.
Soltani-Mohammadi, S., Lak, M., Mohammadi, S., & Karbala, M. A. (2021). Estimation of the Water Table on Different Days of Year by Using Artificial Neural Network GRNN- Case Study: Behbahan Plain. Soft Computing Journal, 3(1), 82-93.
MLA
Saeed Soltani-Mohammadi; Meysam Lak; Sadjad Mohammadi; Mohammad Amin Karbala. "Estimation of the Water Table on Different Days of Year by Using Artificial Neural Network GRNN- Case Study: Behbahan Plain". Soft Computing Journal, 3, 1, 2021, 82-93.
HARVARD
Soltani-Mohammadi, S., Lak, M., Mohammadi, S., Karbala, M. A. (2021). 'Estimation of the Water Table on Different Days of Year by Using Artificial Neural Network GRNN- Case Study: Behbahan Plain', Soft Computing Journal, 3(1), pp. 82-93.
VANCOUVER
Soltani-Mohammadi, S., Lak, M., Mohammadi, S., Karbala, M. A. Estimation of the Water Table on Different Days of Year by Using Artificial Neural Network GRNN- Case Study: Behbahan Plain. Soft Computing Journal, 2021; 3(1): 82-93.
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