Neural Network Models for Air Quality Prediction: A Comparative Study

Transcription

1 Neural Network Models for Air Quality Prediction: A Comparative Study S V Barai A.K.Dikshit Sameer Sharma Department of Civil Engineering IIT Kharagpur

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3 Presentation Outline. Background on Neural Forecaster Objectives Developed Neural Air Quality Predictors Data Collection and Data Analysis Performance Evaluation Results and Discussion Future Projections Closing Remarks

4 Generic Air Quality Forecaster Air Quality Parameter Model Air Quality Forecasts Input Variables Mapping Output Equation or Mathematical Model

5 Neural Air Quality Forecaster Air Quality Parameter Model Air Quality Forecasts Input Variables Mapping Output Equation or Mathematical Model Neural Networks

6 Objectives Data Collection -multiple air quality parameters - Containing daily average pollutant concentrations at a specific location. Implementation of variations of neural network models for predicting air quality. Identification of suitable air quality prediction model(s) for hourly (short-term) data and Yearly (long-term) data. Exhaustive simulation Study using various models. Performance study of models.

7 Implementation of Neural Models Recurrent Network Model (RNM) Change Point Detection Model (CPDM_RNM) Sequential Network Construction Model (SNCM) Self Organizing Feature Maps Model (SOFM)

8 Data Collection and Analysis

9 Case Study 1- Long Term Seven parameters namely VOC (volatile organic carbon), NOX (oxides of nitrogen), CO (carbon monoxide), SO2 (sulphur dioxide), PM10 (particulate matter with size less than 10 microns), PM2.5 (particulate matter with size less than 2.5 microns) and NH3 (ammonia). All concentrations are in micrograms per cubic meter. Annual average data for 15 years from 1985 to 1999 Source:US EPA website Data for 115 counties of California State in USA has been collected

10 Case Study 2- Short Term Three parameters namely RPMA (Respiratory Particulate Matter Average), SO 2 (sulphur dioxide) and NO 2 (nitrogen dioxide) is collected for Delhi State at nine locations. Daily average concentrations for last two years from 3/7/2000 to 20/8/2001. Tata Energy Research Institute web site Data for Ashram Chowk has been used for carrying out simulation

11 Statistical Properties Case Study 1

12 Statistical Properties Case Study 2

13 Model Performance Evaluation

14 Error Evaluation Percentage Error = (target output) / target *100

15 Results and Discussion

16 Case Study 1 Long Term

17 RNM Parameters

18 CPDM_RNM Parameters

19 SNCM Parameters

20 SOFM Model Parameters

21 Models Performance

22 VOC emissions - SOFM Case Study 1 VOC (micg/m3) Year Target Output

23 SO 2 emissions - SOFM Case Study SO2 (micg/m3) Target Output Year

24 PM10 emissions - SOFM Case Study 1 PM10 (micg/m3) Year Target Output

25 Observations The models in general could predict with some accuracy. Self-organizing Feature Map (SOFM) based model has performed extremely well in comparison to other models. Typical results of SOFM for VOC emissions, CO emissions, and NOx emissions results high-light the very good performance of the model. The discrepancy observed in the model prediction can be due to the modeling of the problem.

26 Observations (Contd.) Demonstrated an example of an annual average emission (long-term) data prediction using various neural networks models for a very limited dataset. Models in general have performed reasonably well even with the limited historical data. It is expected that availability of more annual average emission data can improve the performance of models studied.

27 Case Study 2 Short Term

28 RNM Parameters

29 CPDM_RNM Parameters

30 SNCM Parameters

31 SOFM Parameters

32 Models Performance

33 RPMA emissions - SOFM Case Study 2 RPMA (micg/m3) Sample Number Target Output

34 SO 2 emissions - SOFM Case Study 2 SO2 (micg/m3) Target Output Sample Number

35 NO 2 emissions - SOFM Case Study NO2 (micg/m3) Target Output Sample Number

36 Observations Models could predict with modest accuracy. SOFM based model has performed extremely well in comparison to other models. Typical results of SOFM for RPMA emissions, SO2 emissions, and NO2 emissions showed reasonably good match between model predictions with target prediction.

37 Future Projections Models can have as inputs data from multiple sources, such as historical air quality measurements, meteorological data etc. Models can have along with emissions data, episode levels definition, historical measurements of surface and upper air meteorological data. Models should be able to give predictions for the following three different time windows: 1 day, 1 week and 1 month predictions.

38 Future Projections (Contd.) Models should have an easy to use interface and should be able to pre-sent the results in an understandable way to non-computer experts. The model parameters and architecture of models in this project were arrived with trial and error. One can arrive at optimal and better performance model after carrying out systematic studies on networks models and their parameters using optimization techniques such as Genetic

39 Closing Remarks Study was carried out on air quality forecasting using various neural network models: RNM, CPDM_RNM, SNCM and SOFM. The study was focused at preliminary investigation of single variable based time series prediction. The investigation was carried out for long-term as well as short-term air quality data set. Self-Organizing Features Maps (SOFM) used for time series prediction came up as the best tool for time series forecasting.

40 Closing Remarks (Contd.) These were found to be very useful for large training datasets. The results shown here are indications that the neural network techniques can be useful tool in the hands of practitioners of air quality management and prediction. In that case, practitioners need not know even about the development of the model. The models studied in this study are easily implemented, and they can deliver prediction in real time, unlike other modeling techniques. The models can very well easily deal with input noise and uncertainty

41 Pollution Free Solution

42 Acknowledgement Images used in the presentations are obtained from various web-sources. All these web-sources are gratefully acknowledged.