Measuring, monitoring and forecasting system for carbon monoxide concentrations
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Date
2005
Authors
Raji, Usharani
Journal Title
Journal ISSN
Volume Title
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Abstract
The impact of air pollution is broad towards the health of human beings, thus many studies
have been focused on the forecasting of air pollutants in urban areas. In this research, a
portable carbon monoxide forecaster has been developed in order to forecast the future
level of carbon monoxide (CO) concentrations, since CO is primary pollutant which affects
the air quality level. First, a PC based CO forecaster is developed in order to choose the
best neural network and forecasting model. The chosen network together with its modeling
will be later implemented in the portable system. From the comparison study, Hybrid
Multilayered Perceptron (HMLP) network is chosen since it is found to perform better
compared to Multilayered Perceptron (MLP), Recurrent and Radial Basis Function (RBF)
networks, respectively. In this study, the HMLP network is trained with Modified Recursive
Prediction Error (MRPE) algorithm to perform CO concentrations level forecasting.
Besides that, only past CO concentration values are used as network input series, in order
to perform forecasting. Meanwhile, on-line model is chosen since it is found to perform
better compared to off-line model. This is due to its flexibility to update the network
parameters for each data sample. The portable CO forecaster is developed by using
dsPICDEM 1.1 development board, which uses digital signal controller dsPIC30F6014
device. The portable CO forecaster is able to perform CO concentrations measurement
and forecasting up to 8 steps ahead. Besides that, the portable CO forecaster has the
ability to store the measurement and forecasting values, in order to be transferred to PC
through serial communication later on. HMLP network together with on-line model is implemented into the architecture of portable system. The developed portable CO
forecaster is evaluated by using 3 real data sets and 2 simulated environment data sets,
respectively. The R2 values achieved by these data sets for OSA test and 8 steps ahead
forecasting are 0.9805 and 0.8501, 0.9677 and 0.7910, 0.8777 and 0.1524, 0.9966 and
0.8994, 0.9942 and 0.4097, respectively. The portable CO forecaster only uses 5 past CO
concentration values, thus it can be concluded that the portable system gave excellent
results over those data sets. Overall, it can be concluded that the developed portable CO
forecaster can provide accurate and high accuracy of CO concentrations forecasting.
Description
Master
Keywords
Industrial technology , Forecasting System , Carbon Monoxide