The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neuro-fuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells. Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.
Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran
Amir Jalalkamali, Hossein Sedghi, Mohammad Manshouri; Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran. Journal of Hydroinformatics 1 October 2011; 13 (4): 867–876. doi: https://doi.org/10.2166/hydro.2010.034
Download citation file: