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On the use of neural networks for dendroclimatic reconstructions
Author(s) -
D'Odorico Paolo,
Revelli Roberto,
Ridolfi Luca
Publication year - 2000
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/1999gl011049
Subject(s) - artificial neural network , variance (accounting) , feedforward neural network , calibration , regression , computer science , feed forward , dendroclimatology , statistics , machine learning , artificial intelligence , geology , data mining , dendrochronology , mathematics , paleontology , accounting , control engineering , engineering , business
This paper investigates if the use of neural networks can improve the accuracy of tree‐ring based paleoclimatic reconstructions with respect to some of the commonly used methods. A three layers feedforward model of neural network is shown to be very efficient in explaining a high percentage of the variance of the instrumental climatic record both for calibration and validation. Some traditional statistics have been estimated to evaluate the accuracy of the reconstruction. These results have been finally compared with those of a regression‐based model, showing the higher accuracy of the neural network reconstruction.