prediction of groundwater level fluctuation using methods based on machine learning and numerical model

During the recent few decades, the use of various models has been regarded as a promising option to predict groundwater level (gwl) in any given region using a wide variety of data and relevant equations. the lack of trustworthy and comprehensive data is, nevertheless, one of the most significant obstacles that must be overcome in order to analyze and anticipate the depletion of groundwater in the context of water management. because of this, the implementation of artificial intelligence (ai) models that are able to predict the gwl with high accuracy using a reduced amount of data is unavoidable. in this work, the gwl variations of lur plain were simulated using gms model by utilizing the available data and maps. the accuracy of model was assessed at both phases i.e. validation and calibration. following that, ga-ann and ica-ann approaches, together with elm, orelm, and gmdh models, were used in order to fulfill the demand for too smaller volumes by ai procedures. according to the results, the orelm output had the highest correlation with the observed information, which indicates that it is the most accurate model in this regard. the correlation coefficient for this model was 0.976. because of this, instead of utilizing a complicated gms model that needs a significant amount of data for the simulation, an orelm model can be used to reliably forecast the gwl in the lur plain. this simple model allows the researchers to accurately predict changes in gwl during rainy and non-rainy years compared to other complicated and time-consuming numerical models.