Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-95930-6_71 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT I |
Keywords | Field | DocType |
Indoor PM2.5, Genetic algorithm, Ventilation rate | Ventilation (architecture),Pattern recognition,Computer science,Support vector machine,Relative humidity,Artificial intelligence,Air temperature,Random forest,Artificial neural network,Approximation error,Genetic algorithm | Conference |
Volume | ISSN | Citations |
10954 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongjie Wu | 1 | 4 | 5.90 |
| Cheng Chen | 2 | 97 | 32.69 |
| Weisheng Liu | 3 | 0 | 0.34 |
| Ru Yang | 4 | 0 | 0.68 |
| Qiming Fu | 5 | 3 | 5.84 |
| Baochuan Fu | 6 | 5 | 1.11 |
| Dadong Dai | 7 | 0 | 0.34 |