Robust multi-objective calibration strategies – possibilities for improving flood forecasting
Robust multi-objective calibration strategies – possibilities for improving flood forecasting
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DOI：
10.5194/hess-16-3579-2012
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发表时间：
2012-10
影响因子：
6.3
通讯作者：
T. Krausse;J. Cullmann;P. Saile;G. Schmitz
中科院分区：
文献类型：
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作者：
T. Krausse;J. Cullmann;P. Saile;G. Schmitz
面向过程的流域径流模型是为了模拟特定流域内复杂的水文过程，特别是模拟流域出口的流量。这些模型中的大多数表现出高度的复杂性，并且需要通过校准来确定各种参数。最近，自动校准方法变得流行，以识别具有高对应模型性能的参数向量。模型的性能通常是由一个面向目的的目标函数进行评估。实际经验表明，在许多情况下，一个单一的目标函数不能充分描述模型的能力，代表任何方面的集水的行为。这与目标是否是衡量系统行为的不同（可能相反）方面的几个标准的聚合无关。规避这个问题的一个策略是定义多个目标函数，并应用多目标优化算法来确定帕累托最优或非支配解的集合。尽管如此，自动校准程序的主要缺点是将模型校准问题理解为优化问题的解决方案：由于形状复杂的响应曲面，优化问题的估计解决方案可能导致不同的接近最佳的参数向量，这可能导致验证数据的性能非常不同。Bardossy和Singh（2008）以水文模型为例研究了单目标校准问题，并提出了一种称为鲁棒参数估计（ROPE）的几何采样方法。该方法应用数据深度的概念，以克服自动校准程序的缺点，并找到一组强大的参数向量。最近的研究证实了这种方法的有效性。然而，迄今为止发表的所有ROPE方法仅识别相对于单个目标的鲁棒模型参数向量。只有通过聚合才有可能考虑多个目标。在本文中，我们提出了一种方法，结合了多目标优化和基于深度的采样，题为多目标鲁棒参数估计（MOROPE）的原则。它采用多目标优化算法，以确定非支配的鲁棒模型参数向量。随后，它使用Krause和Cullmann（2012 a）中提出的进一步开发的采样算法对具有高数据深度的参数向量进行采样。我们研究所提出的方法的有效性，使用综合测试功能和校准的分布式水文模型，重点是洪水事件在一个小的，前高山，快速响应的流域在瑞士。
Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bardossy and Singh (2008) studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE). This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify robust model parameter vectors with respect to one single objective. The consideration of multiple objectives is just possible by aggregation. In this paper, we present an approach that combines the principles of multi-objective optimisation and depth-based sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE). It applies a multi-objective optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently, it samples parameter vectors with high data depth using a further developed sampling algorithm presented in Krause and Cullmann (2012a). We study the effectivity of the proposed method using synthetical test functions and for the calibration of a distributed hydrologic model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland.