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Estimation of ground water level in Jahrom plain, using Artificial Neural Networks


Nader Barahmand, Mohsen Sabetfard


Vol. 17  No. 6  pp. 72-75


This paper is arranged to estimate the ground water level in Jahrom plain, using Artificial Neural Networks. For this purpose, Artificial Neural Networks (ANNs) with nonlinear activation functions (Logsig or Tansig) were applied. Also, the collected data of Jahrom plain wells in Fars province were used. In this plain, 192 data of monthly average ground water level in 16 years were collected. In addition to ground water level, 4 monthly average parameters: precipitation, moisture, evapotranspiration and temperature were taken into account. These parameters are effective on ground water level. Four statistical parameters (mean square error, relative standard error, scatter index and correlation coefficient) were used to assess the ANN models. Finally, very satisfactory results of the ANNs were obtained.


Artificial Neural Networks (ANNs), Jahrom plain, ground water level and Activation function.