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Determination of Groundwater Level Fluctuations by Artificial Neural Networks

Fatih Üneş, Mustafa Demirci, Zeki Mertcan, Bestami Taşar, Hakan Varçin, Yunus Ziya


Groundwater level change is important in the determination of the efficient use of water resources and plant water needs. Groundwater level fluctuations were investigated using the variable of groundwater level, precipitation, temperature in the present study. The daily data of the precipitation, temperature and groundwater level are used which is taken from PI98-14 observation well station in Minnesota, United States of America. These data, which include information on rainfall, temperature and groundwater level of 2025 daily, were used as input in ANN method. The results were also compared with Multiple Linear Regression (MLR) method. According to this comparison, it was observed that the ANN and MLR method gave similar results for observation. The results show that ANN model will be useful for estimation of groundwater level to monitor possible changes in the future.


Ground water level, Artificial neural networks, Multiple linear regression, Modeling

Volume 3, No 3, SUPPLEMENT I of SYMPOSIUM ARTICLES, pp 35-42, 2018

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