Multivariate analysis of water quality parameters in Lake Palic, Serbia

Horvat, Mirjana; Horvat, Zoltan; Pastor, Kristian

doi:10.1007/s10661-021-09195-8

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…Furthermore, Horvat et al [ 40 ] developed an in-depth analysis of water quality in Lake Palic, Serbia. The analysis was performed by taking water quality measurements for 9 years, from 2011 to 2019.…”

Section: Comparison Of Various Water Quality Monitoring Methodsmentioning

confidence: 99%

“…The analysis was performed by taking water quality measurements for 9 years, from 2011 to 2019. A principal component analysis (PCA) and machine learning classification methods were used to identify a seasonal feature of the water quality, and a fitted model was created via multivariate regression to determine water quality parameters [ 40 ]. Hasan et al also used the multivariate analysis method to determine the quality of groundwater in the northeast part of Bangladesh [ 41 ].…”

Section: Comparison Of Various Water Quality Monitoring Methodsmentioning

confidence: 99%

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Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Zainurin

Ismail

Mahamud³

et al. 2022

IJERPH

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Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

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Section: Comparison Of Various Water Quality Monitoring Methodsmentioning

confidence: 99%

Section: Comparison Of Various Water Quality Monitoring Methodsmentioning

confidence: 99%

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Zainurin

Ismail

Mahamud³

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…BP neural network adopts the gradient descent method to adjust weights, and the adjustment formula is [19]…”

Section: Bp Networkmentioning

confidence: 99%

Construction of Surface Water Pollution Prediction Model Based on Machine Learning

Che

Wei

Wen³

2022

Scientific Programming

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Existing prediction models have low prediction accuracy for surface water pollution with many influencing factors. Taking algal bloom prediction as the entry point of surface water pollution research, LASSO-LARS algorithm is adopted to select the main factors affecting algal bloom variables. At the same time, combined with BP neural network in machine learning, a surface water pollution prediction model based on machine learning is proposed. The results show that the proposed method can accurately predict the algal blooms by using the BP neural network algal bloom prediction model, where the LASSO-LARS algorithm is used to select the variables, such as water temperature, pH, transparency, conductivity, dissolved oxygen, ammonia nitrogen, and chlorophyll a, as model inputs, and the relative error of prediction is less than 5.2%. Thus, the proposed method has certain validity and practical application value.

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“…The work published in [5] presents a case where the Authors used statistical analysis to evaluate the water quality of Lake Prashar in India. Numerous studies opt for this method because of the advantage of implementing statistical analysis to assess water quality data [6,7]. In [7], the researchers used multivariate analysis to evaluate water quality data in Lake Palic.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies opt for this method because of the advantage of implementing statistical analysis to assess water quality data [6,7]. In [7], the researchers used multivariate analysis to evaluate water quality data in Lake Palic.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Water Quality Measurements on the Danube River

Horvat

Pastor

et al. 2021

Water

Self Cite

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This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

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Multivariate analysis of water quality parameters in Lake Palic, Serbia

Cited by 16 publications

References 27 publications

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Construction of Surface Water Pollution Prediction Model Based on Machine Learning

Multivariate Analysis of Water Quality Measurements on the Danube River

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