Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters

• Published in: The Science of the total environment Vol. 648; pp. 839 - 853
• Main Authors: Fijani, Elham, Barzegar, Rahim, Deo, Ravinesh, Tziritis, Evangelos, Skordas, Konstantinos
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.01.2019
• Subjects: aquaculture; autocorrelation; chlorophyll; Complementary ensemble empirical mode decomposition with adaptive noise; data collection; dissolved oxygen; Environmental monitoring; Extreme machine learning; human health; lakes; prediction; Small Prespa Lake; support vector machines; Variational mode decomposition; water quality; Water quality modelling; Variational mode decomposition; Complementary ensemble empirical mode decomposition with adaptive noise; Environmental monitoring; Small Prespa Lake; Water quality modelling; Extreme machine learning

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble emp...