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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
An Abnormal Monitoring Model for
Symbiosis Monitoring Data of Ecological
Environment Based on Density Clustering
1
School of Marxism, Northeastern University, Shenyang 110819, China
2
School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
3
Green Energy Building and Urban Research Institute, Shenyang Jianzhu University, Shenyang 110168, China
Submission date: 2024-01-09
Final revision date: 2024-07-16
Acceptance date: 2024-08-03
Online publication date: 2025-03-27
Publication date: 2025-08-20
Corresponding author
Hua Tang
Green Energy Building and Urban Research Institute, Shenyang Jianzhu University, Shenyang 110168, China
Green Energy Building and Urban Research Institute, Shenyang Jianzhu University, Shenyang 110168, China
Pol. J. Environ. Stud. 2025;34(5):6483-6498
KEYWORDS
density clusteringsymbiosis monitoring data set of ecological environmentdata cleaningMarkov chain Monte Carlo algorithmFSR anomaly data monitoring model
TOPICS
ABSTRACT
Considering the defects of the current abnormal monitoring methods for symbiosis monitoring data
of ecological environments, a method for abnormal monitoring modeling of symbiosis monitoring data
of ecological environments based on density clustering is proposed. A hybrid algorithm of self-adaptive
matrix estimation and random gradient descent is introduced to filter out the dirty data. Genetic
optimization is used to estimate the parameters of incomplete monitoring data and obtain the optimal
data parameters. Based on the optimal parameters, Markov chain and Monte Carlo algorithm are used
to estimate and fill the missing data. The symbiosis monitoring data set of an ecological environment is
divided into extreme cluster, wild value cluster, and normal cluster. The abnormal possibility is given
in different ways in each cluster, and the time sequence diagram of abnormal possibility considering
independent variables and effect quantities is obtained. On this basis, the improved local abnormal
coefficient algorithm is used to set up the abnormal monitoring model of symbiosis monitoring data
of the ecological environment and complete the abnormal monitoring. The experimental results
imply that the method in this paper has high monitoring accuracy, high monitoring efficiency, high
detection rate, and low false detection rate. The proposed method improves the convergence speed
and effectiveness of data cleaning and improves the estimation accuracy of missing data. Therefore, it
can achieve the purpose of optimizing the abnormal monitoring effect of the ecological environment
symbiosis monitoring data.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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