Water quality measurements in rivers are usually performed at intervals of days or months in monitoring campaigns, but little attention has been paid to the spatial and temporal dynamics of those measurements. In this work, we propose scrutinizing the scope and limitations of state-of-the-art interpolation methods aiming to estimate the spatio-temporal dynamics (in terms of trends and structures) of relevant variables for water quality analysis usually taken in rivers. We used a database with several water quality measurements from the Machángara River between 2002 and 2007 provided by the Metropolitan Water Company of Quito, Ecuador. This database included flow rate, temperature, dissolved oxygen, and chemical oxygen demand, among other variables. For visualization purposes, the absence of measurements at intermediate points in an irregular spatio-temporal sampling grid was fixed by using deterministic and stochastic interpolation methods, namely, Delaunay and k-Nearest Neighbors (kNN). For data-driven model diagnosis, a study on model residuals was performed comparing the quality of both kinds of approaches. For most variables, a value of k = 15 yielded a reasonable fitting when Mahalanobis distance was used, and water quality variables were better estimated when using the kNN method. The use of kNN provided the best estimation capabilities in the presence of atypical samples in the spatio-temporal dynamics in terms of leave-one-out absolute error, and it was better for variables with slow-changing dynamics, though its performance degraded for variables with fast-changing dynamics. The proposed spatio-temporal analysis of water quality measurements provides relevant and useful information, hence complementing and extending the classical statistical analysis in this field, and our results encourage the search for new methods overcoming the limitations of the analyzed traditional interpolators.

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.

The pollution of the rivers running through the cities or near to them is a current worldwide problem and requires actions and new technologically available approaches to control and restore those waters. In this work, we hypothesized that last-generation mobile sensor networks can be combined with emergent electrochemical probes and with recently proposed spatio-temporal analysis of the measurement dynamics using machine learning tools. With this purpose, we designed a mobile system to measure five variables: two environmental and three water quality variables in rivers: dissolved oxygen with an electrochemical probe, water temperature, electrical conductivity, air temperature and percentage of relative humidity using solid-state sensors, in each monitoring station. Our main contribution is a first mobile-sensor system that allows mobile campaigns for acquiring measurements with increased temporal and spatial resolution, which in turn allows for better capturing the spatio-temporal behavior of water quality parameters than conventional campaign measurements. Up to 23 monitoring campaigns were carried out, and the resulting measurements allowed the generation of spatio-temporal maps of first and second order statistics for the dynamics of the variables measured in the San Pedro River (Ecuador), by using previously proposed suitable machine learning algorithms. Significantly lower mean absolute interpolation errors were obtained for the set of mean values of the measurements interpolated with Support Vector Regression and Mahalanobis kernel distance, specifically 0.8 for water temperature, 0.4 for dissolved oxygen, 3.0 for air temperature, 11.6 for the percentage relative humidity, and 33.4 for the electrical conductivity of the water. The proposed system paves the way towards a new generation of contamination measurement systems, taking profit of information and communication technologies in several fields.

The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the Discrete Wavelet Transform (DWT) theory and a set of thresholds filters for efficient noise filtering. For the assessment process, we used ECG records from MIT-BIH Arrhythmia database (MITDB) and standardized noise signals (muscle activity and electrode-skin contact) database from the Noise Stress Test database. In addition to the ECG signals a white Gaussian noise present in electrical type signals was added. Furthermore, as a first step we considered baseline wander and power line interference reduction. The metrics used are the Signal-to-Noise Ratio (SNR), the Root Mean Squared Error (RMSE), the Percent Root mean square Difference (PRD), and the Euclidian L2 Norm standard (L2N). Results reveal that there is not a single combination of filtering thresholds (function and value) to minimize all types of noise and interference present in ECG signals. Reason why an ECG denoising algorithm is proposed which allows choosing the appropriate combination (function-value) threshold, where the SNR values were the maximum and the error values were the minimum.

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