Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Abstract

Modeling hydrogeologic processes facilitates in accurate prediction/forecasting of groundwater level variations. Still, the uncertainty in model prediction is a major concern that requires detailed investigation. There could be several factors which introduce uncertainty such as inherent assumption, various levels of model complexity and simplicity. In general, model inputs, parameters and structure are the major sources of uncertainty while quantifying model prediction uncertainty. In this study, a genetic programming (GP) based models have been employed for forecasting groundwater level variation along with prediction uncertainty quantification. Though various sources induce uncertainty in the model prediction, the input uncertainty quantification has received little attention. Hence, the input uncertainty has been considered for the analysis in this study. The proposed method is demonstrated using measured monthly values of rainfall and corresponding groundwater level data of Amarawathi basin, India. It is observed that the prediction along with uncertainty quantification improves the confidence level of models while making decisions, in particular for effective planning and management of groundwater resources.