Application of Bayesian Regularized Neural Networks for Groundwater Level Modeling

Current research uses a novel machine learning method (i.e., Bayesian Regularized Neural Networks; BRNN) to model the groundwater level (GWL) in the Mahabad Aquifer in West Azarbaijan, Iran. Five exploratory factors including the precipitation, evaporation, temperature, outlet streamflow, and GWL (t-1) are considered as inputs to estimate the GWL (t) as a response variable. The mean monthly of datasets for the aquifer from April 2001 to March 2013 (i.e., 12 years) was calculated using the Voronoi map in the ArcGIS based on the data monitoring locations. A ratio of 70/30 was used for model calibration and validation. Evaluation of the results indicated that the model has an excellent performance in the GWL modeling (RMSE = 0. 219; NSE= 0. 908; R-Squared = 0. 910). Importance analysis of the variables indicated that the variables of GWL (t-1), outlet streamflow, temperature, evaporation, and precipitation respectively were the important variables and have a higher contribution in groundwater level prediction.