Prediction of Indoor PM2.5 Index Using Genetic Neural Network Model

Since people spend more than 80% of the daytime in indoor environment every day, the effect on people’s health of the indoor PM2.5 is much greater than outdoor PM2.5. This paper proposes a method based on genetic neural network to predict the indoor PM2.5. We use seven features including indoor ventilation rate, air temperature, relative humidity and others to train the model. The experiment results showed that the relative error is 5.60%, which is 7.55% lower than the traditional artificial neural network, 5.98% lower than the support vector regression method, 8.36% lower than the Random Forest.

This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371), Jiangsu 333 talent project and top six talent peak project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX17_0680).

Authors and Affiliations

1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

   Hongjie Wu, Cheng Chen, Ru Yang, Qiming Fu, Baochuan Fu & Dadong Dai

2. Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China

   Hongjie Wu, Qiming Fu & Baochuan Fu

3. Suzhou Municipal Hospital (North Area), Suzhou, 215000, China

   Weisheng Liu

Corresponding author

Correspondence to Hongjie Wu .

Editors and Affiliations

1. Tongji University, Shanghai, China

   De-Shuang Huang

2. Polytechnic of Bari, Bari, Italy

   Vitoantonio Bevilacqua

3. University of Wollongong, North Wollongong, New South Wales, Australia

   Prashan Premaratne

4. Indian Institute of Technology Kanpur, Kanpur, India

   Phalguni Gupta

Reprints and permissions

© 2018 Springer International Publishing AG, part of Springer Nature

Policies and ethics