Analysis and assessment of confined and phreatic water quality using a rough set theory method in Jilin City, China

2015 ◽  
Vol 15 (4) ◽  
pp. 773-783 ◽  
Author(s):  
He Huang ◽  
Xiujuan Liang ◽  
Changlai Xiao ◽  
Zhong Wang
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Pore Water ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Quality Data ◽  
Confined Water ◽  
Permissible Limits ◽  
Phreatic Water

In groundwater quality assessments it is easier and more effective to reduce the number of parameters included in water quality indices. A total of 20 quaternary loose rock pore water and tertiary clastic rock cranny pore water data sets were used for Jilin City, China, as basic data, and 10 water quality parameters were selected for reduction using rough set theory and a statistical analysis of groundwater quality. Results showed that the quality of confined water was better than that of phreatic water in the study area. Confined water was of good quality, and met the permissible limits of the Quality Standards for Groundwater of China, with the exception of NH4+ and F−. For phreatic water, the five parameters of total dissolved solids, NH4+, NO2−, Fe, and F− exceeded the permissible limits, with levels of NH4+ and Fe having a 70% and 40% rate of exceedance, respectively. The results indicated that water evaluation before and after attribute reduction was consistent, which suggests that through rough set theory redundant parameters in indices were eliminated but the accuracy of water quality classification remained effective, while the complexity of the calculation was reduced. Rough set theory provides a convenient and appropriate way to manage large amounts of water quality data.

scholarly journals Application of Rough Set Theory to Water Quality Analysis: A Case Study

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 50 ◽  
Author(s):  
Maryam Zavareh ◽  
Viviana Maggioni
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Specific Conductivity ◽  
Quality Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Indicators

This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015–December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature.

Inexact Field Learning Approach for Data Mining

2011 ◽  
pp. 1019-1022
Author(s):  
Honghua Dai
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Quality Data ◽  
Learning Approach ◽  
Individual Values ◽  
Machine Learning Approach

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.

Inexact Field Learning Approach for Data Mining

2011 ◽  
pp. 611-614
Author(s):  
Honghua Dai
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Quality Data ◽  
Learning Approach ◽  
Individual Values ◽  
Machine Learning Approach

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.

A rough set theory application in forecasting models

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Haresh Sharma ◽  
◽  
Kriti Kumari ◽  
Samarjit Kar ◽  
◽  
...  
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecasting Models ◽  
Theory Application

Extraction of Geo-spatial Relationship Rules Based on Rough Set Theory: Exemplified by Land Use

2009 ◽  
Vol 11 (2) ◽  
pp. 139-144
Author(s):  
Feng CAO ◽  
Yunyan DU ◽  
Yong GE ◽  
Deyu LI ◽  
Wei WEN
Keyword(s):  
Land Use ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Spatial Relationship

Analysing diabetes using rough sets

2020 ◽  
pp. 533-538
Author(s):  
S. Arjun Raj ◽  
M. Vigneshwaran
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
International Diabetes Federation ◽  
Published Data ◽  
Lower And Upper Approximations

In this article we use the rough set theory to generate the set of decision concepts in order to solve a medical problem.Based on officially published data by International Diabetes Federation (IDF), rough sets have been used to diagnose Diabetes.The lower and upper approximations of decision concepts and their boundary regions have been formulated here.

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