Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

• Published in: Stochastic environmental research and risk assessment Vol. 34; no. 2; pp. 415 - 433
• Main Authors: Barzegar, Rahim, Aalami, Mohammad Taghi, Adamowski, Jan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020; Springer Nature B.V
• Subjects: Aquatic Pollution; Artificial neural networks; Chemistry and Earth Sciences; Chlorophyll; Computational Intelligence; Computer Science; Correlation coefficient; Correlation coefficients; Decision trees; Deep learning; Dissolved oxygen; Earth and Environmental Science; Earth Sciences; Electrical conductivity; Electrical resistivity; Environment; Environmental monitoring; Learning algorithms; Long short-term memory; Machine learning; Math. Appl. in Environmental Science; Neural networks; Original Paper; Oxidation; pH effects; Physics; Predictions; Probability Theory and Stochastic Processes; Regression analysis; Root-mean-square errors; Statistical analysis; Statistics for Engineering; Variables; Waste Water Technology; Water Management; Water monitoring; Water Pollution Control; Water quality; Water quality management; Water resources; Water resources management; Water temperature; Deep learning; Long short-term memory; Water quality modeling; Convolutional neural network

Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memo...