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

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.

Spatio-Temporal River Contamination Measurements with Electrochemical Probes and Mobile Sensor Networks

“…These towns are surrounded by extinct volcanoes that behave as natural barriers to the cold air currents, resulting in a pleasant climate with an average temperature of 15 • C. This environmental condition has transformed this area into an ideal region for residential suburbs and industrial settlements (textile and food). Previous studies have already been conducted on the pollution of Machángara River [14,15], which receives approximately 75% of Quito residual loads, but no research has been performed about the San Pedro River pollution. According to the population and housing census of 2001 [23], the population growth in Los Chillos Valley (micro basin of Guayllabamba) was 16.85% between 2010 to 2015, and it will be 15.13% between 2015 to 2020.…”

“…The proposed system allows us to measure an environmental variable in each location in a short time period and likely moving in space. In previous works [14,15], the dynamics of the variables resulting from the monitoring campaigns were represented using machine learning and advanced interpolation techniques. In this work, the availability of measurements sets for each location allowed us to calculate the mean and the standard deviation, hence yielding a map representation of the first and second order statistics of the spatio-temporal dynamics.…”

“…Second, the Support Vector Machine (SVM) algorithm has been previously used to estimate the spatio-temporal dynamics of contamination measurement in rivers, and it was shown that Mahalanobis and autocorrelation kernels often outperformed k-NN. Here, we scrutinize these three algorithms, which are next summarized [14,15]. We will denote by p j = (d j , t j ) the spatio-temporal coordinate vector, and by f the measured variable or its estimated statistic, i.e., f is here a generalized notation for ν, M, and S, according to the analysis context.…”

“…These conclusions were obtained by working with measurement campaigns at Machángara River. After this [15], we proposed the use of several enhanced statistical interpolation methods, specifically, we included a priori available information of the quality parameter measurements through the use of Support Vector Regression (SVR) algorithms. Two specific kernels showed relevant advantage when they were used for SVR in this application, namely, the Mahalanobis spatial-temporal covariance matrix and the bivariate estimated autocorrelation function.…”

“…With the measurements obtained at nine different places and at different times (November-December, 2017) in the path of the San Pedro River (Ecuador), the spatio-temporal dynamics of the first and second order statistics of the above referenced environmental variables were determined. For the statistical treatment, we adapted previously developed algorithms of machine learning interpolation [14,15]. Measurement maps were built even in difficult-access places and also at different times that were not routinely recorded in the monitoring campaigns.…”