Machine learning driving water suitability assessment of the Yarlungzangbo River, southern Tibetan Plateau

Key Points

• Water suitability assessment indicates significant variations across the Yarlungzangbo River.
• Machine learning models provided an accuracy rate exceeding 85% in predicting water quality.
• Assessment utilized historical water quality metrics and various environmental parameters for analysis.
• Findings highlight the importance of advanced methodologies for effective water resource management in river systems.