Willems Patrick
Hydraulics Laboratory, Katholieke Universiteit Leuven, Kasteelpark Arenberg 40, Leuven, Belgium.
Water Res. 2008 Jul;42(13):3539-51. doi: 10.1016/j.watres.2008.05.006. Epub 2008 May 20.
Quantifiable sources of uncertainty have been identified for a case study of integrated modeling of a sewer system with a more downstream wastewater treatment plant and storage sedimentation tank. The different sources were classified in model input and model-structure-related uncertainties. They were quantified and propagated towards the uncertainty in the event-based prediction of sewer emissions (flow, and physico-chemical water quality concentrations and loads). Based on the concept of variance decomposition, the total prediction uncertainty was split into the contributions of the various uncertainty sources and the different submodels. Although the results strongly depend on the water quality variable considered, it is in most general terms concluded that the uncertainty contribution by the water quality submodels is an order of magnitude higher than that for the flow submodels. Future model improvement should therefore mainly focus on water quality data collection, which would reduce current problems of spurious model calibration and verification, but also of knowledge gaps in in-sewer processes.
对于一个包含更下游污水处理厂和储存沉淀池的下水道系统综合建模案例研究，已识别出可量化的不确定性来源。不同来源被归类为模型输入和与模型结构相关的不确定性。对这些不确定性进行了量化，并将其传播到基于事件的下水道排放预测（流量、物理化学水质浓度和负荷）中的不确定性上。基于方差分解的概念，将总预测不确定性分解为各种不确定性来源和不同子模型的贡献。尽管结果强烈依赖于所考虑的水质变量，但最普遍的结论是，水质子模型的不确定性贡献比流量子模型高一个数量级。因此，未来的模型改进应主要集中在水质数据收集上，这不仅会减少当前虚假模型校准和验证的问题，还会减少下水道过程中知识空白的问题。
