Estimating model prediction error: Should you treat predictions as fixed or random?
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- 作者：Daniel Wallacha ; daniel.wallach@toulouse.inra.fr" class="auth_mail" title="E-mail the corresponding author ; Peter Thorburnb ; peter.thorburn@csiro.au" class="auth_mail" title="E-mail the corresponding author ; Senthold Assengc ; sasseng@ufl.edu" class="auth_mail" title="E-mail the corresponding author ; Andrew J. Challinord ; e ; a.j.challinor@leeds.ac.uk" class="auth_mail" title="E-mail the corresponding author ; Frank Ewertf ; fewert@uni-bonn.de" class="auth_mail" title="E-mail the corresponding author ; eeyshire@uni-bonn.de" class="auth_mail" title="E-mail the corresponding author ; James W. Jonesc ; jimj@ufl.edu" class="auth_mail" title="E-mail the corresponding author ; Reimund Rotterg ; reimund.rotter@luke.fi" class="auth_mail" title="E-mail the corresponding author ; Alex Ruaneh ; alexander.c.ruane@nasa.gov" class="auth_mail" title="E-mail the corresponding author
- 关键词：Crop model ; Uncertainty ; Prediction error ; Parameter uncertainty ; Input uncertainty ; Model structure uncertainty
- 刊名：Environmental Modelling & Software
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：84
- 期：Complete
- 页码：529-539
- 全文大小：484 K
文摘
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It is important to estimate the uncertainty in crop model predictions.
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Two uncertainty criteria are defined, treating predictions as fixed or random.
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The random criterion includes model, parameter and input uncertainty and also bias.
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The random prediction criterion is specific for each prediction situation.
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