Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco

• Published in: Discover applied sciences Vol. 6; no. 6; p. 306
• Main Authors: Bargam, Bouchra, Boudhar, Abdelghani, Kinnard, Christophe, Bouamri, Hafsa, Nifa, Karima, Chehbouni, Abdelghani
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 03.06.2024; Springer Nature B.V
• Subjects: Algorithms; Applied and Technical Physics; Arid zones; Artificial intelligence; Basins; Chemistry/Food Science; Drought; Earth Sciences; Engineering; Environment; Geographic information systems; Hydrology; Learning algorithms; Machine learning; Materials Science; Mountains; Neural networks; Precipitation; Predictions; Regression; Resource management; River basins; Root-mean-square errors; Runoff; Semi arid areas; Semiarid zones; Stream discharge; Stream flow; Streamflow forecasting; Support vector machines; Water resources; Water resources management; Watersheds; Wavelet transforms; Morocco; Atlas Mountains; Streamflow prediction; Machine learning techniques; Support vector regression (SVR); Temporal variability of streamflow; Semi-arid regions; Water resources

Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating M...