APPLICATION OF DATA MINING IN THE ECOLOGICAL ANALYSIS OF THE IMPACT OF BACTERIAL COMMUNITIES IN DIFFERENT RESERVOIRS

2021 ◽  
Author(s):  
Ivana Radojević ◽  
◽  
Aleksandar Ostojić ◽  
Nenad Stefanović
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Bacterial Communities ◽  
Heterotrophic Bacteria ◽  
Open Water ◽  
Mining System ◽  
Routine Monitoring ◽  
Data Mining System ◽  
Using Data ◽  
The Impact

Using data mining techniques, this study analyzes the influence and dependance of bacterial communities that are determined in routine monitoring of open water quality status, such as heterotrophic bacteria (psychrophiles and mesophiles). The SeLaR database was used, which, in addition to various studies of integrated data related to the reservoirs of Serbia, is the basis for advanced data analysis – utilizing statistical methods and data mining. Data for reservoirs with different morphometric qualities, different positions, trophic status, and dominant bacterial community were analyzed. In this research, classification, and analysis of influential parameters, as well as scenario analysis was applied. The results indicate that a designed data mining system can analyze the state and influence of bacterial communities with different parameters that are determined both in standard routine analysis, and in some more specialized studies. This study showed that designed data mining system can serve as flexible, effective, and practical tool for monitoring water quality using bacterial communities in reservoirs.

SU-E-T-140: Automated Daily Patient Treatment Chart Checks Using Data Mining System

2015 ◽  
Vol 42 (6Part14) ◽  
pp. 3364-3364
Author(s):  
A Dhabaan ◽  
X Jiang ◽  
E Schreibmann ◽  
E Elder
Keyword(s):  
Data Mining ◽  
Patient Treatment ◽  
Mining System ◽  
Data Mining System ◽  
Using Data

Surface water quality classification using data mining approaches: Irrigation along the Aladag River*

2021 ◽  
Author(s):  
Mohammad Taghi Sattari ◽  
Hajar Feizi ◽  
Muslume Sevba Colak ◽  
Ahmet Ozturk ◽  
Fazli Ozturk ◽  
...  
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Using Data

An intelligent data mining system for drop test analysis of electronic products

2001 ◽  
Vol 24 (3) ◽  
pp. 222-231 ◽  
Author(s):  
Chi Zhou ◽  
P.C. Nelson ◽  
Weimin Xiao ◽  
T.M. Tirpak ◽  
S.A. Lane
Keyword(s):  
Data Mining ◽  
Drop Test ◽  
Mining System ◽  
Test Analysis ◽  
Electronic Products ◽  
Data Mining System

Water Quality Violations and Avoidance Behavior: Evidence from Bottled Water Consumption

2011 ◽  
Vol 101 (3) ◽  
pp. 448-453 ◽  
Author(s):  
Joshua Graff Zivin ◽  
Matthew Neidell ◽  
Wolfram Schlenker
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Willingness To Pay ◽  
Water Consumption ◽  
Avoidance Behavior ◽  
Bottled Water ◽  
Poor Water Quality ◽  
Using Data ◽  
The Impact

We examine the impact of poor water quality on avoidance behavior by estimating the change in bottled water purchases in response to drinking water violations. Using data from a national grocery chain matched with water quality violations, we find an increase in bottled water sales of 22 percent from violations due to microorganisms and 17 percent from violations due to elements and chemicals. Back-of-the envelope calculations yield costs of avoidance behavior at roughly $60 million for all nationwide violations in 2005, which likely reflects a significant understatement of the total willingness to pay to eliminate violations.

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