Innovative optimization-driven machine learning models for hourly streamflow forecasting

Key Points

• Machine learning models achieved high accuracy for hourly streamflow predictions, improving resource management.
• The predictive modeling showed a significant reduction in forecasting errors compared to traditional methods.
• Assessment utilized optimization techniques to enhance model performance across different river systems and conditions.
• These findings indicate the potential for wider applications, but further validation in diverse environments is necessary.