Knowledge Inference from a Small Water Quality Dataset with Multivariate Statistics and Data-Mining

2017 ◽  
pp. 1-15
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Multivariate Statistics ◽  
Small Water

Predictability of fouling-potential of raw water for ultrafiltration membranes

2011 ◽  
Vol 11 (4) ◽  
pp. 481-489
Author(s):  
S. Krause ◽  
A. Obermayer
Keyword(s):  
Water Quality ◽  
Membrane Fouling ◽  
Large Scale ◽  
High Capacity ◽  
Raw Water ◽  
Ultrafiltration Membranes ◽  
Small Water ◽  
Lab Experiments ◽  
Flux Decline ◽  
Public Drinking Water

The public drinking water supply of southern Germany is characterized by a rather decentralized network. Due to the hydrogeological setting in these parts of Germany many of the small water works with an average capacity of 50 m3/h have to treat raw water extracted from karstic or cliffy aquifers. These raw waters tend to be contaminated with particles and pathogens acquired during snowmelt or after strong rainfalls. In the last decade ultrafiltration has become the technology of choice for the removal of the aforementioned contaminants. Flux decline caused by unanticipated membrane fouling is the main limitation for the application of ultrafiltration membranes. This paper describes how membrane fouling phenomena can be predicted by using a statistical approach based on data from large scale filtration systems in combination with field and lab experiments on raw water quality and membrane performance. The data defines water quality and respective fouling phenomena both in technical scale filtration plants and in lab experiments of eleven different raw waters. The method described here is more economically feasible for small water works when compared to typical pilot experiments that are used for high capacity water works.

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

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

A study of groundwater irrigation water quality in south-central Bangladesh: a geo-statistical model approach using GIS and multivariate statistics

2017 ◽  
Vol 37 (2) ◽  
pp. 193-214 ◽  
Author(s):  
M. Atikul Islam ◽  
Md. Mostafizur Rahman ◽  
Md. Bodrud-Doza ◽  
Md. Iftakharul Muhib ◽  
Mashura Shammi ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Multivariate Statistics ◽  
Irrigation Water ◽  
Irrigation Water Quality ◽  
South Central ◽  
Groundwater Irrigation ◽  
Model Approach

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.

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