Examination and comparison of binary metaheuristic wrapper-based input variable selection for local and global climate information-driven one-step monthly streamflow forecasting

The use of data-driven models to forecast streamflow has received substantial attention from scholars in recent years. However, systematic studies have not been performed to examine binary metaheuristic wrapper-based input variable selection (BMWIVS) in real-world streamflow forecasting. In this study, we explored binary metaheuristic-based shallow machine learning wrappers for one-step monthly streamflow forecasting using local weather information and global climate indices from three catchments with different hydroclimatic conditions. First, the maximal information coefficient (MIC) was employed to investigate the correlations among the forecasting target, streamflow and candidate input variables, which included both local and global climate information. Then, the BMWIVS models obtained by combining eight binary metaheuristic algorithms, five commonly used shallow machine learning algorithms, two combined filter-based input variable selection (FIVS) methods, and two forecasting methods were examined. Finally, the performance of each model was compared with the performance of typical benchmark models, including the univariate seasonal autoregressive integrated moving average model, five machine learning algorithms with no input variable selection, and five machine learning algorithms that use five different FIVS methods. The experimental results emphasized three significant findings. First, an appropriate input variable selection method should be selected in practice because several examined wrappers were inferior to the benchmark models. Second, the BMWIVS model that combined the regularized extreme learning machine method, binary gray wolf optimizer, FIVS results-based initialization method, and forecasted values averaged over multiple runs yielded the best performance in the three cases studied. Third, the correlations in terms of the MIC between the global climate indices and streamflow were lower than those between local weather information and streamflow, and the best wrapper and FIVS would select more local weather information variables than global climate index variables, which suggests that global climate information can be complementary to local weather information for one-step monthly streamflow forecasting. These findings have remarkable practical applications for forecasting monthly streamflow.