Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach
Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach
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DOI：
10.1029/2005wr003986
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发表时间：
2005-07-13
影响因子：
5.4
通讯作者：
McMahon, G
中科院分区：
文献类型：
--
作者：
Qian, SS;Reckhow, KH;McMahon, G
[1]介绍了河网营养盐负荷的贝叶斯非线性回归建模方法，并与最小二乘法进行了比较。研究的目的是为了更好地模拟流域水文和土地利用的空间相关性，以改进模型作为预测工具。贝叶斯建模方法分三步介绍，每一步都有更复杂的模型和数据错误结构。该方法使用来自北卡罗来纳州东部三个大型河流流域的数据集进行了说明。结果表明，贝叶斯模型比传统的最小二乘法更好地考虑了模型和数据的不确定性。讨论了贝叶斯模型在环境水质达标和TMDL评估中的应用。
[1] A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.