Turbidity classification of the Paraopeba River using machine learning and Sentinel-2 images
Keywords:
Machine Learning, Classification, Turbidity, Satellite ImagesAbstract
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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