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Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows
Author(s) -
Nikkhah Amin,
Rohani Abbas,
Rosentrater Kurt A.,
El Haj Assad M.,
Ghnimi Sami
Publication year - 2019
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.13130
Subject(s) - principal component analysis , artificial neural network , computer science , energy (signal processing) , component (thermodynamics) , data set , set (abstract data type) , principal (computer security) , agriculture , production (economics) , energy flow , data mining , artificial intelligence , machine learning , mathematics , statistics , ecology , physics , biology , thermodynamics , operating system , macroeconomics , economics , programming language
There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019