Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management

• Main Authors: Latif S.D., Ahmed A.N.
• Other Authors: 57216081524
• Format: Article
• Published: Springer Science and Business Media B.V. 2024
• Subjects: Boosted regression tree (BRT); Dokan dam; Long short-term memory (LSTM); Reservoir inflow; Brain; Climate change; Economics; Errors; Flood control; Learning algorithms; Learning systems; Mean square error; Reservoirs (water); Water conservation; Water management; Water supply; Boosted regression tree; Boosted regression trees; Long short-term memory; Machine learning algorithms; Root mean square errors; Streamflow prediction; Sustainable water supply; Water supply management; regression analysis; reservoir; sustainable development; water management; water resource; water supply
• Institution: Universiti Tenaga Nasional

As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources