A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach |
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| Authors: | Tzu‐Yi Pai Keisuke Hanaki Han‐Chang Su Lu‐Feng Yu |
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| Institution: | 1. Master Program of Environmental Education and Management, Department of Science Application and Dissemination, National Taichung University of Education, Taichung, Taiwan, ROC;2. Department of Urban Engineering, School of Engineering, University of Tokyo, Bunkyo‐ku, Tokyo, Japan |
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| Abstract: | In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance. |
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| Keywords: | Adaptive network‐based fuzzy inference system Air quality Artificial neural network Ozone Photochemical oxidant |
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