An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and org...

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Vydáno v:Engineering applications of computational fluid mechanics Ročník 15; číslo 1; s. 251 - 271
Hlavní autoři: Sun, Kangjie, Rajabtabar, Mohammad, Samadi, Seyedehzahra, Rezaie-Balf, Mohammad, Ghaemi, Alireza, Band, Shahab S., Mosavi, Amir
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hong Kong Taylor & Francis 01.01.2021
Taylor & Francis Ltd
Taylor & Francis Group
Témata:
Algorithms
artificial intelligence
Contaminants
crow search algorithm
Dissolved solids
Evolutionary algorithms
Gaging stations
Heuristic methods
Machine learning
Mathematical models
multivariate adaptive regression splines
Noise reduction
Optimization
Parameters
Pollutants
Quality management
Search algorithms
Supervised learning
water pollution
Water quality
Water resources management
ISSN:1994-2060, 1997-003X
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Shrnutí:Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and organic matters, and various salts and increasing the concentration of TDS causes the esthetic problems. The reflection of the pollutant burden of the aquatic system can remarkably determined by TDS magnitudes. This study focuses on the prediction of TDS and several biochemical parameters such as Na, Ca, HCO3, and Mg in a river system. To overcome nonstationarity, randomness, and nonlinearity of the TDS data, a multi-step supervised machine learning evolutionary algorithm (MSMLEA) is proposed to improve the model's performance at two gaging stations, namely Rig-Cheshmeh and Soleyman-Tangeh, in the Tajan River, Iran. In addition, a hybrid model that recruits intrinsic time-scale decomposition (ITD) for frequency resolution of the input data as well as a multivariate adaptive regression spline (MARS) were adopted. A novel metaheuristic optimization algorithm, crow search algorithm (CSA), was also implemented to compute the optimal parameter values for the MARS model. To validate the proposed hybrid model, standalone MARS, empirical mode decomposition (EMD)-based models, and hybrid ITD-MARS as well as a MARS-CSA were considered as the benchmark models. Results suggest the ITD-MARS-CSA outperforms other models.
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ISSN:1994-2060
1997-003X
DOI:10.1080/19942060.2020.1861987