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자료유형
학술저널
저자정보
Tikhe, Shruti S. (Department of Civil Engineering, Sinhgad College of Engineering) Khare, K.C. (Department of Civil Engineering Symbiosis Institute of Technology) Londhe, S.N. (Department of Civil Engineering, Vishwakarma Institute of Information Technology)
저널정보
테크노프레스 Advances in environmental research Advances in environmental research 제4권 제2호
발행연도
2015.1
수록면
83 - 104 (22page)

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Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

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