A neural network forecast for daily average PM10 concentrations in Belgium

By J. Hooyberghs, C. Mensink, G. Dumont, F. Fierens, O. Brasseur. 2005

311 citations recorded by the source corpus.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/bb35432e484a8e38990878167ae5403caad7ebc1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Huaicheng Guo
2. Yong Liu
3. Chengzeng You
4. Shaolong Cui
5. Pingjian Yang
6. Guozhu Mao

Citations in the corpus

• A Bayesian hierarchical model for urban air quality prediction under uncertainty (2008, methodology): “A number of methods have been developed for model parameter estimation and pollutant concentration prediction, such as statistical analysis (Huang, 1992), the artificial neural network (ANN) model (Dorling et al., 2003; Hooyberghs et al., 2005; PérezRoa et al., 2006), and the hybrid linear–nonlinear”
• Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. (2017, methodology): “…method (Li et al., 2011), the autoregressive moving average (ARMA)method (Box and Jenkins,1976), the support vector regression (SVR) method (Nieto et al., 2013), the artificial neural network (ANN) method (Hooyberghs et al., 2005), and hybrid methods (Díaz-Robles et al., 2008; Chen et al., 2013).”