Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

2007 ◽  
Vol 22 (1) ◽  
pp. 97-103 ◽  
Author(s):  
S SOUSA ◽  
F MARTINS ◽  
M ALVIMFERRAZ ◽  
M PEREIRA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Ozone Concentrations ◽  
Artificial Neural

scholarly journals Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

2015 ◽  
Vol 2 (4) ◽  
pp. 1317-1337 ◽  
Author(s):  
T. S. dos Santos ◽  
D. Mendes ◽  
R. R. Torres
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Artificial Neural

Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

scholarly journals Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

2016 ◽  
Vol 23 (1) ◽  
pp. 13-20 ◽  
Author(s):  
T. Soares dos Santos ◽  
D. Mendes ◽  
R. Rodrigues Torres
Keyword(s):  
Climate Change ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
South America ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Northeastern Brazil ◽  
Monthly Precipitation ◽  
Artificial Neural

Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Agroclimatology-Based Yield Model for Carrot Using Multiple Linear Regression and Artificial Neural Networks

2013 ◽  
Vol 105 (3) ◽  
pp. 863-873 ◽  
Author(s):  
Arumugam Thiagarajan ◽  
Rajasekaran R. Lada ◽  
Sivakami Muthuswamy ◽  
Azure Adams
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Yield Model ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document