Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

作者：Lim JungJin; Kim YongSu; Oh TaeSuk; Kim MinJung; Kang OnYu; Kim Jeong Tai; Kim In Won; Kim Jo Chun; Jeon Jae Sik; Yoo ChangKyoo^*

来源：Korean Journal of Chemical Engineering, 2012, 29(8): 994-1003.

DOI：10.1007/s11814-011-0278-z

• Indoor Air Quality (IAQ)
• Pollution Prediction
• Fisher%26apos
• s Linear Discriminant (FLD)
• Platform Screen Door (PSD) System
• Variable Selection

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM10, CO2, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher%26apos;s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.

• 出版日期2012-8