Water quality evaluation in representative lake based on principal component analysis

2011 ◽  
Author(s):  
Caihong Liu ◽  
Lignag Xu ◽  
Haiying Gao ◽  
Hongbo Zhao ◽  
Jizheng Pan ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Water Quality Evaluation

scholarly journals Daily water quality evaluation of reservoir and cyanobacteria pollution index calculation

2020 ◽  
Author(s):  
Wen Zhang ◽  
Zhiwei Liu
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Pollution Index ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Factor ◽  
Water Quality Evaluation ◽  
Factor Evaluation ◽  
Index Calculation

Abstract Water quality evaluation is the most direct and quantitative description of reservoir water environment. In view of the lack of biological factor evaluation in water quality evaluation at present, firstly the conventional pollution index evaluation system was used to evaluate the water quality continuously. Then the correlation between the evaluation indexes and the concentration of cyanobacteria was studied. After determining the correlation, a novel calculation method of cyanobacteria pollution index was determined by principal component analysis (PCA). The result showed that the eutrophication index and nitrogen phosphorus index of the water body were high. Biological pollution in reservoir cannot be ignored. The correlation between eutrophication index and cyanobacteria concentration only reached a weak correlation (Pearson correlation = 0.242). For the reservoir, it is necessary to establish a special cyanobacteria pollution index. Five variables participated cyanobacteria pollution index calculation by principal component analysis (PCA) method. The total variance of the two main components was 77.107%, which can reflect most of the data information. In the reservoirs of other areas, similar parameters can also be selected to calculate the cyanobacteria pollution index. This research provide reference for the biological factor evaluation of similar reservoirs in the world.

Application of Principal Component Analysis in Water Quality Evaluation by SPSS Software

2011 ◽  
Vol 403-408 ◽  
pp. 3277-3280 ◽  
Author(s):  
Jun Fang Gu ◽  
Chang Jun Zhu ◽  
Zhen Chun Hao
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Component Model ◽  
Water Quality Evaluation ◽  
Multiple Variables ◽  
Spss Software ◽  
Principal Component Model

In view of the defect of traditional water quality evaluation model, principal component analysis method is developed to evaluate surface water quality in Baoying country. By SPSS software, principal component model is applied to evaluate water quality at representative sections in Baoying surface area. Principal component analysis is a way to reduce orginal dimension, to make multiple variables inti a few comprehensive index. By the combination of variables index, adjusting the combinatorial coefficient to make the new variables representative independent. The process is introduced in the paper in detail.The results indicate that principal component model is suitable for water quality evaluation. By analysis, it is important to pay attention to bring into effective measures for pollution control.

Water Quality Evaluation for Tributaries of Danjiangkou Reservoir with Principal Component Analysis

2014 ◽  
Vol 955-959 ◽  
pp. 3586-3594 ◽  
Author(s):  
Xiao Kang Xin ◽  
Wei Yin ◽  
Hai Yan Jia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Water Quality Evaluation ◽  
Mean Values ◽  
Danjiangkou Reservoir ◽  
Research Task ◽  
Pca Method ◽  
Water Quality Status

To reflect the water quality status of Danjiangkou reservoir tributaries, identify the main pollution factors, and compare pollution degree between tributaries. Principal component analysis (PCA) method is used to assess and explicate research task with annual mean values for 11 main water quality indicators of 16 priority tributaries measured in 2013. The results show that: The main pollution factors of Danjiangkou reservoir are oxygen-consuming pollutants and heavy metal, and the former is the dominant one. Shending, Sihe, Jianghe, Jianhe and Danjiang are heavily-polluted tributaries while Duhe, Jiangjun, Taogou, Hanjiang and Taohe are lightly-polluted tributaries.

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

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