Key Words: water quality, water quantity, climate, machine learning, digital twin, remote sensing
We propose to integrate advanced machine learning algorithms and remote sensing technologies into digital twins of hydropower plants and other infrastructures. This innovative approach aims to predict and manage water quality and quantity and optimize power generation, thereby increasing
operational resilience and efficiency. It will also employ predictive analytics within the digital twin to forecast future trends in water quality and quantity based on historical data, real-time observations, and climate models.
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