A new approach to predict daily pH in rivers based on the 'a trous' redundant wavelet transform algorithm
Article
Article Title | A new approach to predict daily pH in rivers based on the 'a trous' redundant wavelet transform algorithm |
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ERA Journal ID | 1992 |
Article Category | Article |
Authors | Rajaee, Taher (Author), Ravansalar, Masoud (Author), Adamowski, Jan F. (Animator) and Deo, Ravinesh C. (Author) |
Journal Title | Water, Air and Soil Pollution: an international journal of environmental pollution |
Journal Citation | 229 (3) |
Year | 2018 |
Place of Publication | Netherlands |
ISSN | 0049-6979 |
1567-7230 | |
1573-2932 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11270-018-3715-3 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11270-018-3715-3 |
Abstract | Prediction of pH is an important issue in managing water quality in surface waters (e.g., rivers, lakes) as well as drinking water. The capacity of artificial neural network (ANN), wavelet-artificial neural network (WANN), traditional multiple linear regression (MLR), and wavelet-multiple linear regression (WMLR) models to predict daily pH levels (1, 2, and 3 days ahead) at the Chattahoochee River gauging station (near Atlanta, GA, USA) was assessed. In the proposed WANN model, the original time series of pH and discharge (Q) were decomposed (after being split into training and testing series) into several sub-series by the the à trous (AT) wavelet transform algorithm. The wavelet coefficients were summed to obtain useful input time series for the ANN model to then develop the WANN model for pH prediction. The redundant à trous algorithm was used for data decomposition. Model implementation indicated the values of 1-day-ahead pH predicted by the WANN model closely matched the observed values (with a coefficient of determination, R2 = 0.956; Root Mean Square Error, RMSE = 0.019; and Mean Absolute Error, MAE = 0.015). It is therefore possible that the WANN model’s accuracy can be attributed to its better predictive ability (due to the use of the AT) to remove the noise caused by pH shifts (e.g., acid precipitation). Peak pH values predicted by the WANN model were also closer to observed values compared to the other machine learning models |
Keywords | artificial neural network; multiple linear regression; ‘a trous’ algorithm; river water; quality pH |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
469999. Other information and computing sciences not elsewhere classified | |
460207. Modelling and simulation | |
410102. Ecological impacts of climate change and ecological adaptation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Qom, Iran |
McGill University, Canada | |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q49ww/a-new-approach-to-predict-daily-ph-in-rivers-based-on-the-a-trous-redundant-wavelet-transform-algorithm
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