Insights into enhanced machine learning techniques for surface water quantity and quality prediction based on data pre-processing algorithms

• Published in: Journal of hydroinformatics Vol. 24; no. 4; pp. 875 - 897
• Main Authors: Panahi, Javad, Mastouri, Reza, Shabanlou, Saeid
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.07.2022
• Subjects: Aggregation; Algorithms; bagging algorithm; Chemical oxygen demand; Datasets; Decision trees; Decomposition; decomposition–ensemble model; Economic development; Error reduction; Forecasting; Frequency analysis; Frequency dependence; Gene expression; Hydrology; karoon river; Learning algorithms; Machine learning; Neural networks; Nonlinear systems; Nonlinearity; Pollutants; Pollution control; Resource management; Runoff; Sediments; Stream discharge; Stream flow; Surface water; Time-frequency analysis; variational mode decomposition; Water pollution; Water pollution control; Water quality; water quality and quantity; Water resources; Water resources management; Water supply; Watershed management; Watersheds; Wavelet transforms; Iran; United States > US; China

Quality and quantity of streamflow are crucial components in the management and control of water resources according which are challenging due to their nonstationarity and uncertainty path. This paper presented an ensemble data pre-processing-based machine learning (ML) algorithm for the decision-su...