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Comparison of using regression modeling and an artificial neural network for herbage dry matter yield forecasting
Author(s) -
Majkovič Darja,
O'Kiely Padraig,
Kramberger Branko,
Vračko Marjan,
Turk Jernej,
Pažek Karmen,
Rozman Črtomir
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2770
Subject(s) - artificial neural network , regression , linear regression , statistics , regression analysis , yield (engineering) , mathematics , econometrics , dry matter , artificial intelligence , computer science , agronomy , materials science , metallurgy , biology
This study presents an application of artificial neural network and regression modeling techniques for forecasting grassland dry matter yield. Using data from a field plot experiment on semi‐natural grassland in Maribor (Slovenia), the multiple regression and artificial neural network methodologies were employed to explain the patterns of dry matter yield during a 6‐year period. On the basis of the two proposed approaches forecasts were conducted for the independent, validation year (6). The results in terms of Theil inequality coefficient, mean absolute error, and correlation coefficient show a better forecasting performance for the artificial neural network (likely due to the non‐linear relationships prevailing among regressors and regressand) while relationships between observables can be better explained by regression modeling results. Copyright © 2016 John Wiley & Sons, Ltd.