A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation
A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation
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DOI：
10.1016/j.envsoft.2016.09.007
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发表时间：
2016-11-01
影响因子：
4.9
通讯作者：
Bouman, Bas
中科院分区：
文献类型：
--
作者：
Confalonieri, Roberto;Bregaglio, Simone;Bouman, Bas
对于大多数生物物理领域，模型结构的差异很少被量化。在这里，我们使用基于分类学的方法来表征十三种水稻模型。识别了分类键和每个键的二元属性，并使用二元相似性度量和算术平均值的未加权配对方法将模型分为五个簇。对四个地点的模型输出进行主成分分析。结果表明，(i) 结构差异通常会导致相似的预测，(ii) 相似的结构可能会导致模型输出存在巨大差异。校准期间的用户主观性可能隐藏了模型结构和行为之间的预期关系。如果这一解释得到证实，则强调需要共享协议来减少校准过程中的自由度，从而限制用户主观性影响模型性能的风险。 (C) 2016 Elsevier Ltd. 保留所有权利。
For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. (C) 2016 Elsevier Ltd. All rights reserved.