Comparative study of groundwater level forecasts using hybrid neural network models

Groundwater is the world's central supply of fresh water. Water supply policies, particularly in dry seasons, thus need to be based on accurate modelling of groundwater level (GWL) fluctuations. In the work reported in this paper, a hybrid wavelet-transform-based extreme learning machine (ELM) model was investigated for predicting GWL. Two other popular models - a wavelet-transform based artificial neural network and a wavelet-transform-based adaptive neuro-fuzzy interference system - were used to evaluate the model. GWL data and mean temperatures of observation wells in an Iranian watershed between 1981 and 2017 were used in the study. The performance of the models was assessed be evaluating their root mean square error, correlation coefficient and mean absolute error. The wavelet-transform-based ELM model outperformed the other two models with a correlation coefficient of 0.983 during a 1 month period. The model was also superior to the others in terms of training and testing speeds.