Volume 26, Issue 2 (ُSummer 2022)                   JWSS 2022, 26(2): 167-186 | Back to browse issues page


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zarif F, asareh A, Asadiloor M, fathian H, khodadadi dehkordi D. Prediction of Groundwater Level Using Artificial Neural Networks Based on Efficient Input Variables Selection by Partial Mutual Information Algorithm. JWSS 2022; 26 (2) :167-186
URL: http://jstnar.iut.ac.ir/article-1-4098-en.html
Islamic Azad University, Ahvaz Branch , ali_assareh_2003@yahoo.com
Abstract:   (163 Views)
An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.
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Type of Study: Research | Subject: Ggeneral
Received: 2020/11/24 | Accepted: 2021/08/24 | Published: 2022/09/1

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