Statistical Analysis of Uncertainty Propagation and Model Accuracy
Statistical Analysis of Uncertainty Propagation and Model Accuracy
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不确定性传播和模型精度的统计分析
DOI：
10.1007/978-3-642-82054-0_14
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发表时间：
1983
期刊：
影响因子：
--
通讯作者：
D. McLaughlin
中科院分区：
文献类型：
--
作者：
D. McLaughlin
直到最近，水质模型的不确定性和预测准确性的主题在很大程度上被忽视。这有很多原因，包括一个普遍的信念，即模型预测可以像所希望的那样准确，只要增加控制方程的细节和复杂性。对复杂模型结构的热情导致了复杂生态系统模型的激增，这些模型变得越来越大，包括越来越多的生物区室，化学相互作用等。不幸的是，模型大小和复杂性的增加并不一定能提供预期的预测精度的提高。如果说有什么不同的话，那就是它们使模型更难使用，结果更难解释。很明显，在许多应用中，限制模型性能的主要因素不是缺乏细节，而是模型输入不够准确。
Until recently the subjects of model uncertainty and prediction accuracy were largely ignored by water-quality modelers. There were many reasons for this, including a widespread conviction that model predictions could be made as accurate as desired simply by increasing the detail and complexity of the governing equations. Enthusiasm for complex model structures led to a proliferation of sophisticated ecosystem models, which grew larger and larger and included more and more biological compartments, chemical interactions, etc. Unfortunately, increases in model size and complexity did not necessarily provide the expected improvements in prediction accuracy. If anything, they made the models more difficult to use and the results harder to interpret. It became apparent that the primary factor limiting model performance in many applications was not lack of detail but rather insufficiently accurate model inputs.