investigating groundwater level fluctuations using group method of data handling and empirical bayesian kriging models (case study: silakhor plain)

Groundwater level fluctuations and the lack of reliable methods for estimating them are major contributors to land subsidence. data mining has increasingly applied artificial intelligence (ai) techniques in recent years to predict time series variations, including groundwater level changes. in this study, a temporal–spatial hybrid model was developed by integrating the group method of data handling (gmdh) with empirical bayesian kriging (ebk) to predict monthly groundwater levels. the gmdh model was employed to extrapolate temporal variations one month ahead, while the ebk model interpolated spatial variations to generate regional groundwater level maps. the silakhor plain in iran was chosen as the subject of the case study. the model was built using monthly data from 11 groundwater stations that were collected between 2003 and 2013. the hybrid model employed groundwater level observations and precipitation records as inputs. results indicated that the gmdh-ebk model provided reliable and accurate predictions, with strong correlations in both training and testing phases. the model achieved coefficients of determination of 0.95, 0.91, 0.85, and 0.79 for the hamyaneh, chaghadon, sugar factory, and valyan wells, respectively. overall, the proposed methodology represents a significant advancement in regional groundwater modelling and offers a promising approach to supporting sustainable water resource management.