Model Structure Error and Model Complexity in Groundwater Modeling

小行星9526645 地下水模拟的可靠性往往是值得怀疑的，主要是因为模型校准的问题尚未完全解决。 分布参数模型的校正原则上应包括以下两个方面：模型结构的确定和未知模型参数的估计。 然而，迄今为止，模型结构的确定由于其固有的困难而受到有限的关注。 在实践中，复杂含水层往往是根据有限的地质信息和主观猜测，用一个简化的概念模型来表示的。 因此，模型结构误差是不可避免的。 在大多数情况下，模型故障是由显著的模型结构错误引起的。 文献回顾表明，目前还没有系统的方法来估计和减少这种类型的错误。 如果这一具有挑战性的问题能够得到解决或部分解决，地下水模拟将得到显着改善，模型应用的可靠性显着增加。 建议的研究的主要目标包括：模型结构识别的新概念的发展，模型结构误差估计的有效算法的发展，和模型结构歧视的数据收集策略的发展。 最重要的目标是发展一个系统的方法模型结构的确定。 用模型A代替模型B的模型结构误差被定义为它们在观测空间和预测空间中的距离的加权和。 然后将模型结构误差的计算转化为一个最大-最小问题。 当含水层系统的真实的结构未知时，将使用逐步回归程序来估计模型结构误差，并确定模型结构复杂性的适当水平。 一个启发式程序，结合变分灵敏度分析将被用来解决建议的最大-最小优化问题。 在南加州的一个主要地下水流域收集的现场数据将用于验证所提出的方法。

9526645 Yeh The reliability of groundwater modeling is often questionable, primarily because the problem of model calibration has not been completely resolved. In principle, the calibration of a distributed parameter model should include the following two aspects: the determination of model structure and the estimation of the unknown model parameters. To date, however, the determination of model structure has received limited attention because of its inherent difficulty. In practice, a complex aquifer is often represented by a simplified conceptual model based on limited geological information and subjective guess. As a result, model structure error is unavoidable. In most cases, model failure is caused by significant model structure error. Literature review indicates that there exist no systematic methods to estimate and to reduce such type of error. If this challenging problem can be solved or partly solved, groundwater modeling will be significantly improved and the reliability of model applications significantly increased. Major objectives of the proposed research include: the development of new concepts for model structure identification, the development of efficient algorithms for model structure error estimation, and the development of data collection strategy for model structure discrimination. The overriding objective is the development of a systematical methodology for model structure determination. The model structure error of using model A to replace model B is defined as a weighted sum of their distances in both the observation and prediction spaces. The calculation of model structure error is then formulated into a maximum-minimum problem. When the real structure of an aquifer system is unknown, a stepwise regression procedure will be used to estimate the model structure error and to determine an appropriate level of model structure complexity. A heuristic procedure that combines with the variational sensitivity analysis will be used to solve the proposed maxim um-minimum optimization problems. Field data collected at a major groundwater basin in Southern California will be used to validate the proposed methodology.