Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution

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.

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Anomaly Detection Based on Selective Segmentation Row-Column Two-Dimensional Principal Component Analysis for Hyperspectral Images

Laser & Optoelectronics Progress ◽

10.3788/lop55.081002 ◽

2018 ◽

Vol 55 (8) ◽

pp. 081002

Author(s):

杨桄 Yang Guang ◽

向英杰 Xiang Yingjie ◽

王琪 Wang Qi ◽

田张男 Tian Zhangnan

Keyword(s):

Principal Component Analysis ◽

Anomaly Detection ◽

Principal Component ◽

Component Analysis ◽

Hyperspectral Images ◽

Two Dimensional

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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 ◽

10.3390/w12020420 ◽

2020 ◽

Vol 12 (2) ◽

pp. 420 ◽

Cited By ~ 1

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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