An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting

In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to streamflow forecasting for input variable selection (IVS), comparative analysis of different random forest-based IVS (RF-IVS) methods is yet absent. Here, we investigate performance of five RF-IVS methods in four data-driven models (RF, support vector regression (SVR), Gaussian process regression (GP), and long short-term memory (LSTM)). A case study is implemented in the contiguous United States for one-month-ahead streamflow forecasting. Results indicate that RF-IVS methods enable to acquire enhanced performance in comparison to widely used partial Pearson correlation and conditional mutual information. Meanwhile, performance-based RF-IVS methods appear to be superior to test-based methods, and the test-based methods tend to select redundant variables. The RF with a forward selection strategy is finally recommended to connect with GP model as a promising combination having potential to yield favorable performance.