Ozone Level Forecasting: Time Series Analysis Using Multi-Layer Perceptron (MLP) Artificial Neural Networks Trained with Bayesian Regulation Back-propagation
Publish place: International Conference on Architecture, Urban Planning, Civil Engineering, Art and Environment; Future horizons, look to the past
Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICAUCAE01_0599
تاریخ نمایه سازی: 10 تیر 1396
Abstract:
Concerns of tropospheric ozone effects on human life motivate decision makers to forecast ozone concentration specially in metropolitan and tropic regions. Due to nonlinearity of ozone variations, using neural networks methods is considered a proper tool for forecasting air quality. This paper proposes a multi-layer perceptron (MLP) model trained with Bayesian Regulation Back-Propagation (BRP) algorithm for forecasting maximum daily ozone concentrations in Tucson city. The performance of proposed model trained with BRP algorithm shows better results than other training algorithms in which R=0.9627 (Pearson correlation coefficient) for measured and predicted data and the coordination between their errors and the standard normal distribution curve are reasons of our claim. Achieved results approve that proposed model has a fair forecasting of ozone concentrations time series.
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Authors
Davood Mohammady Maklavany
Petroleum University of Technology, Abadan, Khuzestan, IRAN
Yunes Jeddi
Petroleum University of Technology, Abadan, Khuzestan, IRAN
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