Cantabrian Basin Authority

Oviedo, Spain

Cantabrian Basin Authority

Oviedo, Spain

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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: 2016

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes when the productions of algae and other aquatic vegetations are increased, and deterioration of water quality and all its uses in general. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on multivariate adaptive regression splines (MARS) approach in combination with the artificial bee colony (ABC) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the MARS training procedure, which significantly influences the regression accuracy. 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 is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.85 for the Total phosphorus estimation and 0.84 for the Chlorophyll concentration were obtained when this hybrid ABC-MARS-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, conclusions of this innovative research work are exposed. © 2016 Elsevier B.V..


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.


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.


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.


Garcia Nieto P.J.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | Sanchez Lasheras F.,University of Oviedo | de Cos Juez F.J.,University of Oviedo | Diaz Muniz C.,Cantabrian Basin Authority
Science of the Total Environment | Year: 2012

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational water uses. The aim of this study is to improve our previous and successful work about cyanotoxins prediction from some experimental cyanobacteria concentrations in the Trasona reservoir (Asturias, Northern Spain) using the multivariate adaptive regression splines (MARS) technique at a local scale. In fact, this new improvement consists of using not only biological variables, but also the physical-chemical ones. As a result, the coefficient of determination has improved from 0.84 to 0.94, that is to say, more accurate predictive calculations and a better approximation to the real problem were obtained. Finally the agreement of the MARS model with experimental data confirmed the good performance. © 2012 Elsevier B.V.


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.


Alonso Fernandez J.R.,Cantabrian Basin Authority | Diaz Muniz C.,Cantabrian Basin Authority | Garcia Nieto P.J.,University of Oviedo | de Cos Juez F.J.,University of Oviedo | And 2 more authors.
Ecological Engineering | Year: 2013

Cyanobacteria are one of the major concerns to public health since some of them produce a range of potent toxins (cyanotoxins). This group of microorganism can be present in drinking and recreation waters representing a health risk for animals and human being. For this reason, as prevention, it is important to bring forward their presence. In this study, using physical-chemical and biological parameters, a hybrid approach based on genetic algorithms (GAs) combined with the multivariative adaptative regression splines (MARS) technique, was developed and applied for forecasting the presence of cyanobacteria in a water reservoir (Trasona reservoir, Northern Spain) and in consequence, the cyanotoxin risk. The significance of each biological and physical-chemical variables used for its determination was assessed and a predictive model useful for preventing the presence of cyanobacteria, and consequently of cyanotoxins, was defined. © 2012 Elsevier B.V.


Garcia Nieto P.J.,University of Oviedo | Alonso Fernandez J.R.,Cantabrian Basin Authority | De Cos Juez F.J.,University of Oviedo | Sanchez Lasheras F.,University of Oviedo | Diaz Muniz C.,Cantabrian Basin Authority
Environmental Research | Year: 2013

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. © 2013 Elsevier Inc.


Vilan Vilan J.A.,University of Vigo | Alonso Fernandez J.R.,Cantabrian Basin Authority | Garcia Nieto P.J.,University of Oviedo | Sanchez Lasheras F.,University of Oviedo | And 2 more authors.
Water Resources Management | Year: 2013

Cyanobacteria also known as blue-green algae can be found in almost every conceivable environment. Cyanobacteria blooms occur frequently and globally in water bodies and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Consequently, anticipation of cyanotoxins presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines and multilayer perceptron networks from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training centre of canoeing in the Northern Spain). The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxins presence in the reservoir is presented through the model. Secondly, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed. © 2013 Springer Science+Business Media Dordrecht.


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.

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