Use of ultraviolet-visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index
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Data
2018-06-01
Autores
Alves, Edson Marcelino [UNESP]
Rodrigues, Ramon Juliano [UNESP]
Correa, Caroline dos Santos [UNESP]
Fidemann, Tiago [UNESP]
Rocha, Jose Celso [UNESP]
Lemos Buzzo, Jose Leonel [UNESP]
Neto, Pedro de Oliva [UNESP]
Fernandez Nunez, Eutimio Gustavo
Título da Revista
ISSN da Revista
Título de Volume
Editor
Springer
Resumo
The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to simplify this index determination by replacing traditional analytical methods with ultraviolet-visible (UV-Vis) spectrophotometry associated with artificial neural network (ANN). A total of 100 water samples were collected from two rivers located in Assis, SP, Brazil and calculated the WQI by the conventional method. UV-Vis spectral analyses between 190 and 800 nm were also performed for each sample followed by principal component analysis (PCA) aiming to reduce the number of variables. The scores of the principal components were used as input to calibrate a three-layer feed-forward neural network. Output layer was defined by the WQI values. The modeling efforts showed that the optimal ANN architecture was 19-16-1, trainlm as training function, root-mean-square error (RMSE) 0.5813, determination coefficient between observed and predicted values (R-2) of 0.9857 (p < 0.0001), and mean absolute percentage error (MAPE) of 0.57% +/- 0.51%. The implications of this work's results open up the possibility to use a portable UV-Vis spectrophotometer connected to a computer to predict the WQI in places where there is no required infrastructure to determine the WQI by the conventional method as well as to monitor water body's in real time.
Descrição
Palavras-chave
Water quality index, UV-Vis spectrophotometry, Artificial neural networks, Principal component analysis, Water pollution, Water analysis
Como citar
Environmental Monitoring And Assessment. Dordrecht: Springer, v. 190, n. 6, 15 p., 2018.