Surface Water Quality Evaluation Based on Bayesian Network

Xie, Xiaohui; Liu, Ying; Luo, Yulan; Du, Qianying

doi:10.2112/si93-008.1

Cited by 3 publications

(2 citation statements)

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“…How to classify, process, nd, and identify useful data in the database becomes very important and meaningful. Conventional data mining or data recognition technologies include various classical data mining and classi cation algorithms such as probability and statistics methods and fuzzy logic methods [1][2][3]. As a probability network that can graphically represent the relationship between random variables, Bayesian network data structure is essentially a causal learning network.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Structure Learning and Application

Zhang

Liu

Wang

et al. 2022

Mobile Information Systems

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With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level; the algorithm proposed in this study has obvious advantages in bibliometrics.

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Section: Introductionmentioning

confidence: 99%

Bayesian Network Structure Learning and Application

Zhang

Liu

Wang

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

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“…There have been increasing attempts in recent years to evaluate water quality using Bayesian inference and modelling (e.g. Liang et al 2016;Xie et al 2019;Worrall et al 2020). The Bayesian parametric approach quantifies the uncertainty in the statistics by assuming a particular shape of a population distribution; but the resultant statistics can be biased if the assumption is inappropriate (Krueger 2017).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking inference methods for water quality monitoring and status classification

Jung

Senf

Jordan

et al. 2020

Environ Monit Assess

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River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

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