Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

• Published in: Cogent engineering Vol. 9; no. 1
• Main Authors: Mohammed, Sarah J., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, Hashim, Khalid
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithms; data pre-processing; Environment models; Geoteknik; hybrid model; Lakes; Machine learning; meta-heuristic algorithms; Monitoring; Optimization; Parameters; Research methodology; Soil Mechanics; Water circulation; Water level fluctuations; Water level forecasting; Water levels; Water quality; Watersheds

The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and...