A Rough Set Based Model in Water Quality Analysis

Abstract

Due to pollution caused by the expansion of human activities and economic development, water quality has gradually deteriorated in many areas of the world. Therefore, analysis of water quality becomes one of the most essential issues of modern civilization. Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex phenomenon of water quality, have gained attention in recent years. With the ability to deal with both numeric and nominal information, and express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many fields. However, the application of RST has not been widely investigated in water quality analysis. The reducts generated by RST models become very time-consuming as the size of the problem increases. Using multinomial logistics regression (MLR) techniques to provide reducts of RST models, this investigation develops a hybrid Multinomial Logistic Regression and Rough Set Theory (MLRRST) model to analyze relations between degrees of water pollution and environmental factors in Taiwan. Empirical results indicate that the MLRRST model could analyze water qualities efficiently and accurately, and yield decision rules for the staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality.

Dispersion Coefficient Prediction Using Empirical Models and ANNs

“…They are capable of providing a neurocomputing approach to solving complex problems. In the last decade, ANNs have been employed in the solution of various water resources problems, such as rainfall-runoff modelling (Loukas et al 2002;Sentil-Kumar et al 2005), groundwater problems (Mantoglou 2003;Garcia and Shigidi 2006) and water quality problems (Diamantopoulou et al 2007;Akratos et al 2008;Dogan et al 2009;Pai and Lee 2010). ANNs have also been applied to complete missing data in hydrology, meteorology and water quality modelling.…”

Modelling adolescent pedestrian crossing decision at unmarked roadway

“…rough set theory [12,13] uses information systems to represent knowledge and deal with vague data. Some basic rough set concepts are as follows.…”

“…This paper adopted the cloud model [10,11] to describe the cognitive uncertainty for influencing factors in pedestrian crossing decision and discretize the continuous attribute data. Then adolescent pedestrian crossing decision table was constructed and the rough set method [12,13] was used to extract crossing decision rules expressed in the form of if-then statements, which make knowledge have a clear meaning.…”

Forecasting the Stock Market with Linguistic Rules Generated from the Minimize Entropy Principle and the Cumulative Probability Distribution Approaches

“…Since the development of the original exposition of the rough set theory (RST) as a method of set approximation, it has continued to flourish as a tool for data mining [17,[22][23][24].…”

“…In this paper, in order to avoid any possible intrusions of the model designer's subjective predictions, based on technical analytical methods, one objective, automatic, artificial intelligence model is proposed, which combines three data mining techniques into forecasting processes: (1) MEPA (minimize entropy principle approach), which subdivides data into membership functions [14][15][16][17][18]; (2) CPDA (cumulative probability distribution approach), which fuzzifies the observations into linguistic values based on the cumulative probability of the observations [17,19,20]; and (3) rough set theory [17,19,[21][22][23][24], which mines rules from the linguistic dataset. Using these techniques, objective and effective rules can be produced as the basis for forecasting.…”