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Open AccessArticle

Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

1
Graduate Programme in Environmental Engineering Sciences, São Carlos Engineering School, University of São Paulo, São Carlos 13566-590, Brazil
2
Advanced Visualization & Geoinformatics Lab—VizLab, Unisinos University, São Leopoldo 93022-750, Brazil
3
Graduate Programme in Applied Computing, Unisinos University, São Leopoldo 93022-750, Brazil
4
Graduate Programme in Biology, Unisinos University, São Leopoldo 93022-750, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(9), 2580; https://doi.org/10.3390/su11092580
Received: 14 March 2019 / Revised: 21 April 2019 / Accepted: 22 April 2019 / Published: 5 May 2019
(This article belongs to the Special Issue Sustainability in the Development of Water Systems Management)
The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations. View Full-Text
Keywords: suspended solids; unmanned aerial vehicle; spectral imaging; artificial neural networks suspended solids; unmanned aerial vehicle; spectral imaging; artificial neural networks
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MDPI and ACS Style

Guimarães, T.T.; Veronez, M.R.; Koste, E.C.; Souza, E.M.; Brum, D.; Gonzaga, L., Jr.; Mauad, F.F. Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images. Sustainability 2019, 11, 2580. https://doi.org/10.3390/su11092580

AMA Style

Guimarães TT, Veronez MR, Koste EC, Souza EM, Brum D, Gonzaga L Jr., Mauad FF. Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images. Sustainability. 2019; 11(9):2580. https://doi.org/10.3390/su11092580

Chicago/Turabian Style

Guimarães, Tainá T.; Veronez, Maurício R.; Koste, Emilie C.; Souza, Eniuce M.; Brum, Diego; Gonzaga, Luiz, Jr.; Mauad, Frederico F. 2019. "Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images" Sustainability 11, no. 9: 2580. https://doi.org/10.3390/su11092580

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