A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

By L. Diaz-Robles, Juan. C. Ortega, Joshua S. Fu, Gregory D. Reed, J. Chow, John G. Watson, Juan A. Moncada-Herrera. 2008

408 citations recorded by the source corpus.

Subfields: Environmental Engineering, Health, Toxicology and Mutagenesis

How researchers see this paper

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

1. F. Las-Heras
2. C. Ordóñez
3. Francisco Javier de Cos Juez
4. Javier Roca-Pardiñas
5. Wei-Jia Lai
6. Jing-Tang Kao
7. H. Lo
8. S. Tseng

Citations in the corpus

• Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. (2017, methodology): “5 concentrations: temperature, humidity, wind speed and visibility (Díaz-Robles et al., 2008; Saide et al., 2011; Guocai, 2004).”
• Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models (2013, methodology): “[12] developed a hybrid model combining ARIMA and ANN to predict extreme events of particle emission in a city in Chile.”
• Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan (2011, methodology): “Díaz-Robles et al. (2008) used four types of model including multiple linear regression (MLR) model, Box–Jenkins time series (ARIMA) model, ANN, and a hybrid ARIMA-ANN model to forecast PM in urban areas.”