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 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.