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Sensors 2017, 17(10), 2357; https://doi.org/10.3390/s17102357

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

1
Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
2
Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y de Computación, Universidad Rey Juan Carlos, Camino del Molino s/n, 28943 Fuenlabrada, Spain
3
Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain
4
Centro de Nanociencia y Nanotecnología, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador
*
Author to whom correspondence should be addressed.
Received: 19 July 2017 / Revised: 26 September 2017 / Accepted: 12 October 2017 / Published: 16 October 2017
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Abstract

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. View Full-Text
Keywords: water quality; pollution measurements; spatio-temporal interpolation; support vector regression; Mahalanobis kernel; autocorrelation kernel water quality; pollution measurements; spatio-temporal interpolation; support vector regression; Mahalanobis kernel; autocorrelation kernel
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Vizcaíno, I.P.; Carrera, E.V.; Muñoz-Romero, S.; Cumbal, L.H.; Rojo-Álvarez, J.L. Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods. Sensors 2017, 17, 2357.

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