ABSTRACT Miami-Dade County, FL, has complex hydrological and water management conditions, creating challenges for predicting groundwater levels. Due to the potential impacts of increasing sea level and changes in rain patterns on flooding and saltwater intrusion, it is essential to be able to accurately predict groundwater levels and to understand the drivers of groundwater levels. The method traditionally used to predict groundwater levels in south Florida and elsewhere is through the implementation of a numerical model. This research looks at machine learning techniques as an alternative method to provide improved predictions of groundwater levels. Extreme gradient boosting is one of the machine learning techniques that uses algorithms to train a model by identifying hidden patterns and relationships between groundwater level drivers and groundwater levels. The accuracies of the extreme gradient boosting model and a numerical model were compared by computing the root mean square error as a model performance measure. Comparing these two methods showed that the extreme gradient boosting model had better accuracy in predicting groundwater levels at the majority of groundwater monitoring sites. Furthermore, comparison of these two models helped us to identify problematic areas where both models performed poorly and narrow down the factors that could cause this poor model performance. Finally, the extreme gradient boosting method gave the importance of each feature included among the potential groundwater level drivers, including the ranked importance of a particular factor and its lag time, at each of the considered groundwater level monitoring sites.
Rogers et al. (Mon,) studied this question.