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Iglesias C.,University of Vigo | Martinez Torres J.,Centro Universitario Of La Defensa | Garcia Nieto P.J.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | And 3 more authors.
Water Resources Management | Year: 2014

Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000/60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed. © 2013 Springer Science+Business Media Dordrecht. Source


Diaz Muniz C.,Cantabrian Basin Authority | Garcia Nieto P.J.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | Martinez Torres J.,Centro Universitario Of La Defensa | Taboada J.,University of Vigo
Science of the Total Environment | Year: 2012

Water quality controls involve large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears to deviate markedly from other members of the sample in which it occurs. An interesting analysis is to find those observations that produce measurements that are different from the pattern established in the sample. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using oxygen and turbidity as indicator variables. Until now, methods were based on considering the different parameters as a vector whose components were their concentration values. Our approach lies in considering water quality monitoring through time as curves instead of vectors, that is to say, the data set of the problem is considered as a time-dependent function and not as a set of discrete values in different time instants. The methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in San Esteban estuary. Results were discussed in terms of origin, causes, etc., and compared with those obtained using the conventional method based on vector comparison. Finally, the advantages of the functional method are exposed. © 2012 Elsevier B.V. Source


Alonso Fernandez J.R.,Cantabrian Basin Authority | Garcia Nieto P.J.,University of Oviedo | Diaz Muniz C.,Cantabrian Basin Authority | Alvarez Anton J.C.,Electronics and Computer Systems
Ecological Engineering | Year: 2014

The aim of this study was to obtain a predictive model able to perform an early detection of eutrophication using as predictors the chlorophyll concentration of the previous days. In this research work, the evolution of chlorophyll in the Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. For this purpose, some biological parameters (phytoplankton species expressed in biovolume) in addition to the most important physical-chemical parameters are considered. The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication in the reservoir is presented through the model. Secondly, a model for forecasting eutrophication is obtained. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, conclusions of this innovative research work are exposed. © 2014 Elsevier B.V. Source


Di Blasi J.I.P.,University of Vigo | Martinez Torres J.,Centro Universitario Of La Defensa | Garcia Nieto P.J.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | And 2 more authors.
Ecological Engineering | Year: 2013

Water quality controls help to prevent pollution and to protect public health as well as to maintain and improve the biological integrity of the water bodies, for which, authorities establish water quality standards. Water quality controls involve a large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears strongly deviate from other members of the sample in which it occurs. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using turbidity, conductivity and ammonium as indicator variables. Up to now, methods were based on considering the different parameters as a vector whose components were their concentration values. This innovative approach lies in considering water quality monitoring over time as continuous curves instead of as discrete points, i.e., the dataset studied is considered as a time-dependent function instead of as a set of discrete values in different time instants. This new methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in the Miño river basin with success. Results of this study are discussed here in terms of origin, causes, etc. Finally, the conclusions as well as the advantages of the functional method are exposed. © 2013 Elsevier B.V. Source


Garcia Nieto P.J.,University of Oviedo | Garcia-Gonzalo E.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | Diaz Muniz C.,Cantabrian Basin Authority
Ecological Engineering | Year: 2014

Water quality controls involve mainly a large number of measurements of chemical and physical-chemical variables. In this sense, turbidity is shown as a key variable in water quality control because it is an integrative parameter. Consequently, the aim of this work is focused on this main parameter and how it is been influenced by other water quality parameters in order to simplify water quality controls since they are expensive and time consuming. Taking into account that support vector machines (SVMs) have been used in a wide range of biological problems with promising results, this paper proposes a practical new hybrid model for long-term turbidity values forecasting based on SVMs in combination with the particle swarm optimization (PSO) technique. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, turbidity values have been predicted here by using the hybrid PSO-SVM-based model from the remaining measured water quality parameters (input variables) in the Nalón river basin (Northern Spain) with success. The agreement of the PSO-SVM-based model with experimental data confirmed the good performance of this model. Finally, the main conclusions of this study are exposed. © 2014 Elsevier B.V. Source

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