Novel approaches for predicting efficiency in helically coiled tube flocculators using regression models and artificial neural networks

2019 ◽  
Vol 34 (4) ◽  
pp. 550-562
Author(s):  
D. S. Oliveira ◽  
E. C. Teixeira ◽  
C. B. Donadel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Regression Models ◽  
Coiled Tube ◽  
Helically Coiled Tube ◽  
Novel Approaches ◽  
Artificial Neural

scholarly journals ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

2018 ◽  
Vol 36 (4) ◽  
pp. 891
Author(s):  
Ouorou Ganni Mariel GUERA ◽  
José Antônio Aleixo SILVA ◽  
Rinaldo Luiz Caraciolo FERREIRA ◽  
Héctor Barrero MEDEL ◽  
Daniel Álvarez LAZO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Regression Models ◽  
Growth Models ◽  
Pinus Caribaea ◽  
Three Stages ◽  
Artificial Neural ◽  
Sigmoidal Growth ◽  
Pinus Caribaea Morelet

The present study was carried out to compare the performances of regression models and Artificial Neural  Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type  and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which  Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three  stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

scholarly journals Comparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear

2019 ◽  
Vol 11 (14) ◽  
pp. 216 ◽  
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Cactus Pear ◽  
Federal Institute ◽  
The Mean ◽  
Artificial Neural

Behavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.

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