Dr. Kenneth Belitz - Old problems, new approach: Applications of Ensemble-Tree Machine Learning to Hydrogeology

Ensemble tree modeling is a machine learning method well suited for representing complex non-linear phenomena. As such, ensemble tree modeling can be applied to a wide range of questions in hydrogeology, including questions related to hydrogeologic mapping. Some questions are problems of regression in which one seeks an estimate of a continuous variable. For example, what is the depth to the water table across a region of interest? Other questions are problems of classification. For example, across a region of interest and over a range of depths, is groundwater oxic or reduced?

The U.S. Geological Survey National Water Quality Assessment project (NAWQA) has used ensemble tree methods to address questions related to groundwater quality at regional and national scales. Some of our models evaluate the three-dimensional distribution of factors that can affect groundwater quality, such as pH, redox, and groundwater age. In turn, the modeled factors were used in subsequent models to map the three-dimensional distribution of contaminant concentrations. In our experience, ensemble tree models are a powerful tool for answering difficult questions. They can be used as a complement to process-based modeling and to make predictions at scales that preclude the use of process-based approaches.