  Predictions are a central part of water resources research. Historically,
physically-based models have been preferred; however, they have largely failed
at modeling hydrological processes at a catchment scale and there are some
important prediction problems that cannot be modeled physically. As such,
machine learning (ML) models have been seen as a valid alternative in recent
years. In spite of their availability, well-optimized state-of-the-art ML
strategies are not being widely used in water resources research. This is
because using state-of-the-art ML models and optimizing hyperparameters
requires expert mathematical and statistical knowledge. Further, some analyses
require many model trainings, so sometimes even expert statisticians cannot
properly optimize hyperparameters. To leverage data and use it effectively to
drive scientific advances in the field, it is essential to make ML models
accessible to subject matter experts by improving automated machine learning
resources. ML models such as XGBoost have been recently shown to outperform
random forest (RF) models which are traditionally used in water resources
research. In this study, based on over 150 water-related datasets, we
extensively compare XGBoost and RF. This study provides water scientists with
access to quick user-friendly RF and XGBoost model optimization.
