Acute exposure to high concentrations of pollutants during exercise resulted in decline in cardiovascular functions and hematological parameters in healthy athletes.

In this study, Artificial Intelligence (AI) models along with ensemble techniques were employed for predicting the SSL via single-station and multi-station scenarios. Feed Forward Neural Networks (FFNNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR) were the employed AI models, and the simple averaging (SA), weighted averaging (WA), and neural averaging (NA) were the ensemble techniques developed for combining the outputs of the individual AI models to gain more accurate estimations of the SSL. For this purpose, twenty-year observed streamflow and SSL data of three gauging stations, located in Missouri and Upper Mississippi regions were utilized in both daily and monthly scales. The obtained results of both scenarios indicated the supremacy of ensemble techniques to single AI models. The neural ensemble demonstrated more reliable performance comparing to other ensemble techniques. For instance, in the first scenario, the ensemble technique increased the predicted results up to 20% in the verification phase of the daily and monthly modeling and up to 5 and 8% in the verification step of the second scenario.