Suk-Hyun Yu and Heeyong Kwon
Input Data Balancing in a Neural Network PM10 Forecasting System
1210 - 1214
2017
11
11
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10008220
https://publications.waset.org/vol/131
World Academy of Science, Engineering and Technology
Recently PM10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.
Open Science Index 131, 2017