Turbidity classification of the Paraopeba River using machine learning and Sentinel-2 images



Machine Learning, Classification, Turbidity, Satellite Images


The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socio-environmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of satellite images, widely available at low cost, emerges as a relevant alternative. This work proposes a systematic experimental evaluation of five machine learning methods - Extra Trees, Multilayer Perceptron, Naïve Bayes, Random Forest and Support Vector Machine, under different configurations and input data treatments, to classify the turbidity of the Paraopeba River waters from Sentinel-2 mission images. In a classification setup defined from Brazilian legislation turbidity classes, all methods obtained results equal to or greater than 0.87 accuracy, with appropriate settings and data treatments. The best result was obtained using the Support Vector Machine classifier with hyperparameters adjustment by random search, input data processed by the Yeo-Johnson transformation and selection of spectral bands by collinearity analysis. In this case, the accuracy was 0.96 with two classes, and 0.91 with 3 classes, indicating the feasibility of using this method to classify turbidity.


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Author Biography

Leonardo V. Batista, Centro de Informática da Universidade Federal da Paraiba, Joao Pessoa, Paraíba, Brasil

Professor Leonardo Vidal Batista (Ph.D., M.Sc) received the B.Sc. and the M.Sc degrees in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro, Brazil, in 1990 and in 1993, respectively, and the Ph.D. degree from Federal University of Campina Grande, Brazil, in 2002 and 2013. From 1990 and 1993, he worked at the Scientific Center of IBM Brazil, on a research project on satellite image classification, a partnership with the National Institute for Space Research (INPE). From 1993 to 1994, he worked on a project involving the use of satellite images for meteorology, conducted by INPE. During 1994-1995, he was with the Computer Science and Statistics Department of the Federal University of Piauí, Brazil. Since then, he is a Professor at the Computer Systems Department of the Federal University of Paraíba, Brazil. His main research fields include digital signal and image processing and analysis, artificial intelligence and machine learning.


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How to Cite

Vidal Batista, L. (2021). Turbidity classification of the Paraopeba River using machine learning and Sentinel-2 images. IEEE Latin America Transactions, 20(5), 799–805. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6051