Systematic Identification of DO-BOD Model Structure
Systematic Identification of DO-BOD Model Structure
复制标题
DO-BOD模型结构的系统识别
DOI：
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发表时间：
1976
期刊：
影响因子：
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通讯作者：
P. Young
中科院分区：
文献类型：
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作者：
B. Beck;P. Young
扩展卡尔曼滤波器 (EKF) 为基于确定性模型响应误差（表面）最小化的现有模型拟合方法提供了基于逻辑统计的扩展。然而，其作为识别基础的关键特征是算法的递归性质，它允许估计模型参数中可能的变化。根据这种估计的变化是否现实，考虑到动态系统的物理性质，可以制定模型充分性的标准。这种模型识别方法应用于基于日常现场数据的淡水河流系统中 DO-BOD 相互作用的建模问题。结果发现，由 Streeter-Phelps 方程的动态版本定义的基本模型结构是不够的，在考虑河流的情况下，有必要引入额外的持续阳光项来解释漂浮藻类种群的影响。
The Extended Kalman Filter (EKF) provides a logical statistically based extension to those existing approaches to model fitting based on deterministic model response error (surface) minimization. Its crucial feature as a basis for identification, however, is the recursive nature of the algorithm which permits the estimation of possible variations in the model parameters. Depending upon whether such estimated variations are realistic or not, bearing in mind the physical nature of the dynamic system, it is possible to formulate criteria for model adequacy. This approach to model identification was applied to the problem of modeling DO-BOD interaction in a freshwater river system based on daily field data. It was found that the basic model structure, as defined by a dynamic version of the Streeter-Phelps equations, was inadequate and, in the case of the river considered, it was necessary to introduce additional sustained sunlight terms to account for the effects of floating algal populations.