Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile

By P. Perez, A. Trier, J. Reyes. 2000

331 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1a6a092cc72871c34c4c46cea02635032e473d6e

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In Environmental Engineering

In Health, Toxicology and Mutagenesis

Citations in the corpus

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach (2011, unknown): “These statistics are given by: PE = 〈|Pi − Oi |〉〈Oi〉 (Perez et al ., 2000) (16) WI=1 − ∑ i |Pi − Oi | ∑ i (|Pi − O| + |Oi − O”
Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models (2013, methodology): “[7] compared the forecasting produced by three different methods (MLP, multiple linear regression (MLR), and persistence methods).”
The inclusion of exogenous variables in functional autoregressive ozone forecasting (2002, background): “Boznar et al. (1993), Yi and Prybutok (1996), Gardner and Dorling (1999) and Pérez et al. (2000) considered the multilayer perceptron while Ruiz-Suarez et al. (1995) chose bidirectional associative memory and holographic associative memory models.”
Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan (2011, methodology): “In the study proposed by Pérez et al. (2000), ANN, linear perceptron model, and persistence model were used to predict hourly average concentrations of PM2.”
Synoptic and meteorological drivers of extreme ozone concentrations over Europe (2016, background): “Moreover, it has been shown that model performance increases by including persistence of air quality variables (Pérez et al 2000, Smith et al 2000, Grivas and Chaloulakou 2006).”
Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration (2010, methodology): “The literature demonstrates that AI models such as ANN and neuro-fuzzy techniques are successfully used for air pollutionmodeling (Nunnari et al., 2004; Perez-Roaa et al., 2006) and forecasting (Perez et al., 2000; Gautama et al., 2008).”