Real-time Monitoring of Pollutant Diffusion States and Source Using Fuzzy Adaptive Kalman Filter

An inverse analysis method for the real-time monitoring of pollutant diffusion is developed based on fuzzy adaptive Kalman filter (FAKF) coupled with weighted recursive least squares algorithm (WRLSA). In the monitoring process, the discrete diffusion states equation is established first. Then, the...

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Bibliographic Details
Published in:Water, air, and soil pollution Vol. 229; no. 7; pp. 1 - 14
Main Authors: Wang, Xudong, Zhang, Daqian, Chen, Liying
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.07.2018
Springer
Springer Nature B.V
Subjects:
Adaptive filters
Algorithms
Analysis
Atmospheric Protection/Air Quality Control/Air Pollution
Climate Change/Climate Change Impacts
Computer simulation
Diffusion
Dye dispersion
Earth and Environmental Science
Environment
Environmental monitoring
equations
Hydrogeology
Kalman filters
Mathematical models
Methods
Monitoring
Pollutants
pollution
Pollution monitoring
Real time
Soil Science & Conservation
Technology application
Water Quality/Water Pollution
Inverse analysis
Diffusion state
Fuzzy adaptive Kalman filter
Real-time monitoring
Pollutant source
ISSN:0049-6979, 1573-2932
Online Access:Get full text
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Summary:An inverse analysis method for the real-time monitoring of pollutant diffusion is developed based on fuzzy adaptive Kalman filter (FAKF) coupled with weighted recursive least squares algorithm (WRLSA). In the monitoring process, the discrete diffusion states equation is established first. Then, the FAKF is adopted to realize the precise monitoring of the pollution diffusion states while the WRLSA is used to monitor the pollutant source in real time. Finally, the simulations are presented to validate the effectiveness of the technique, which shows that this technique has wide applications in situations with several different kinds of sources and measurement noises. Besides, the results demonstrate the strong robustness of this method to have great monitoring performance.
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ISSN:0049-6979
1573-2932
DOI:10.1007/s11270-018-3885-z