Predicting the Groundwater level by Adaptive Neuro-Fuzzy Inference System 
(Case Study: Qom plain)

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Salamatian Seyed Amin; Abrari Hossien; Nazari Atiyeh

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Irrigation and Water Engineering Year:2023 | Volume:13 | Issue:51 Page(s): 285-304

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Information Journal Paper

Title

Predicting the Groundwater level by Adaptive Neuro-Fuzzy Inference System (Case Study: Qom plain)

Author(s)

Salamatian Seyed Amin | Abrari Hossien | Nazari Atiyeh | Issue Writer Certificate

Keywords

Qom Plain

Modeling

aquifer

predicting the groundwater level

Adaptive Neuro-Fuzzy Model

Abstract

Groundwater is considered the most important water source in the Qom Plain. Therefore, predicting the groundwater level fluctuations of this plain can be a great help for planning and decision-making. The main aim of this study is to predict the groundwater level of Qom Plain by the Adaptive Neuro-Fuzzy Inference System. It is easily possible to study the process of nonlinear models such as groundwater without considering the physics of the problem or knowing the characteristics of aquifer layers and complex geological information by this method. In this study nine observation wells were selected in Qom Plain. Different patterns and combinations including groundwater level, well discharge, and rainfall in the earlier 12 months were used for input data and groundwater level in the current month was selected for output in the model. According to the results, the mentioned model have had suitable accuracy in predicting the groundwater level with respect to the correlation coefficient (R2) equal to 0.96 and the root mean square error equal to 0.26. The use of well discharge data in Modeling improved the results in observation wells, which shows that these data have an effect on decreasing the groundwater level of this plain. Results showed that drainage from wells is the most important factor in reducing the groundwater level of this plain. The results also illustrated that the groundwater level of previous months and the amount of well drainage are suitable for predicting the groundwater level in arid and semi-arid climates such as Qom Plain at the Modeling entrance.

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