Prediction of the Quality of Fresh Water in a Basin

Prediction of the Quality of Fresh Water in a Basin

Sira M. Allende (Universidad de La Habana, Cuba), Daniel C. Chen (Smith and King College, UK), Carlos N. Bouza-Herrera (Universidad de La Habana, Cuba), Agustin Santiago (Universidad Autónoma de Guerrero, Mexico) and Jose Maclovio Sautto (Universidad Autónoma de Guerrero, Mexico)
DOI: 10.4018/978-1-5225-0997-4.ch019
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Derivatives play an important role in social and economic studies. They describe the behavior of conditional expectations. Once a phenomena is characterized by parametric specifications, the conditional expectation m(x) may be modeled by a regression function. Then, derivatives may be computed by fitting the regression function. In applications, parametric estimators are commonly used, because of the un-knowledge of other more effective methods. The validity of a regression fitting approach depends on the knowledge of certain aspects related with the true functional form. In this paper, we develop a study on the usage of soft computing methods for providing an alternative to the use of non-parametric regression. We develop our modeling including neural networks and rough sets approaches. The studied problem is the eutrophication due to the growth of the population of algae. Real life data is provided by a study on a fresh water basin. They are used for developing a comparison of different approaches. A methodology is recommended for implementing a monitoring system of the water quality.
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Classification is one of the most used techniques of the multivariate static tool-box. It may be described roughly as a technique concerned with assigning data cases (i.e. observations) to one of a fixed number of possible classes, see Sharma (1996). Classifing is present when dealing with the needs grouping. It has been developed firstly by statisticians. Machine learning researchers have given new insights both from the theoretical and computation issues of classification. They consider that classification is a process that permits learning the existence of patterns. It ends fixing how cases must be classified different predetermined classes of data. See discussion on this problem in Brooks (2010), Hastie-Tibshirani-Friedman (2001). Once the model is built, it can be used to classify new data. Hence, the goal of Classification is sorting observations into two or more labeled classes. The emphasis of it is on deriving a rule that can be used to optimally assign new objects to the classes.

When classifying we assign a membership to each data item (case). Each record is related to a variable of interest 978-1-5225-0997-4.ch019.m01, which allows classifying, and a set of covariates 978-1-5225-0997-4.ch019.m02. Statisticians denominate the covariables “independent variables” and computer scientists “input variables”. A functional relationship between 978-1-5225-0997-4.ch019.m03 and 978-1-5225-0997-4.ch019.m04 should be described. Take a case (observation) 978-1-5225-0997-4.ch019.m05. We evaluate, where 978-1-5225-0997-4.ch019.m06, is the vector of attributes measured in case k and 978-1-5225-0997-4.ch019.m07 represents a vector of parameters which 978-1-5225-0997-4.ch019.m08 weights the importance of each coordinate of 978-1-5225-0997-4.ch019.m09.

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