论文标题
评估与样本量相关的环境模型性能的统计参数：海洋颜色遥感中的案例研究
Statistical parameters for assessing environmental model performance related to sample size: Case study in ocean color remote sensing
论文作者
论文摘要
环境模型性能需要使用一些统计参数进行评估，例如平均绝对误差（MAE）和均方根误差（RMSE）。这些参数的优势和缺点仍然存在争议。这项研究的目的是将统计参数（类型A型不确定性（UA））引入模型性能评估中。我们特别关注样本量和三个评估参数之间的关系，并测试了一些海洋色遥感算法和数据集。结果表明，RMSE，MAE和UA都随样本量N而变化，但呈现不同的趋势。因此，基于我们的测试结果和理论分析，我们得出结论，UA比RMSE和MAE更好，以表达模型不确定性，因为其向下趋势表明，我们采用的样本越多，不确定性就越少。 RMSE和MAE是评估模型准确性而不是不确定性的良好参数。
Environmental model performances need to be assessed using some statistical parameters, such as mean absolute error (MAE) and root mean square error (RMSE). The advantages and disadvantages of these parameters are still in controversial. The purpose of this study is to introduce a statistical parameter, type A uncertainty (UA), into model performance evaluations. We particularly focus on the relations between sample sizes and three evaluation parameters, and tested a few ocean color remote sensing algorithms and datasets. The results indicate that RMSE, MAE and UA all vary with the sample size n but present different trends. Based on our tested results and theoretical analysis, we therefore conclude that UA is better than RMSE and MAE to express model uncertainty, because its downward trends indicate that the more samples we take, the less uncertainty we get. RMSE and MAE are good parameters for assessing model accuracy rather than uncertainty.
