Hydraulic head change predictions in groundwater models using a probabilistic neural network

Groundwater resource management is an increasingly complicated task that is expected to only get harder and more important with future climate change and increasing water demands resulting in an increasing need for fast and accurate decision support systems. Numerical flow simulations are accurate but slow, while response matrix methods are fast but only accurate in near-linear problems. This paper presents a method based on a probabilistic neural network that predicts hydraulic head changes from groundwater abstraction with uncertainty estimates, that is both fast and useful for non-linear problems. A generalized method of constructing and training such a network is demonstrated and applied to a groundwater model case of the San Pedro River Basin. The accuracy and speed of the neural network are compared to results using MODFLOW and a constructed response matrix of the model. The network has fast predictions with results similar to the full numerical solution. The network can adapt to non-linearities in the numerical model that the response matrix method fails at resolving. We discuss the application of the neural network in a decision support framework and describe how the uncertainty estimate accurately describes the uncertainty related to the construction of the training data set.