Selection and Justification of Hydro-Morphodynamic Models using Information Theory: Active Learning on Surrogate Emulators

使用信息论选择和论证水形态动力学模型：代理模拟器的主动学习

• 批准号: 513054523
• 负责人: Professor Dr.-Ing. Wolfgang Nowak
• 金额: --
• 依托单位: Lehrstuhl für Wasserbau und Wassermengenwirtschaft
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513054523?language=en
• 关键词: Selection; Justification; Hydro; Morphodynamic; Models

Modelling hydro-morphodynamic processes in river ecosystems faces the challenges to reproduce complex, dynamic, and highly variable systems by making expert-driven simplification hypotheses. For this reason, a model for reproducing hydro-morphodynamics over long spatio-temporal scales, for instance, for climate change analysis, involves vast uncertainty. The input data required for modelling hydro-morphodynamics involve information on ecosystem characteristics, such as sediment grain size and surface elevation. Yet, every dataset has gaps in time or in space with often considerable uncertainty. Thus, the modelling procedure involves a chain of data acquisition and processing, and substantial simplifications of complex systems, which result in various types of uncertainty. These steps (and their weaknesses) in the modelling chain constitute substantial research challenges regarding uncertainty quantification for sophisticated hydro-morphodynamic models. Moreover, the selection of multi-dimensional hydro-morphodynamic modelling concepts is challenging since a multitude of different modelling approaches exist that need justified decisions. Therefore, hydro-morphodynamic modelling can benefit from rigorous and statistical methods for model selection, callibration and justification. To address these modelling challenges at feasible computational costs, our project proposes a machine learning approach based on Bayesian analysis, information theory, and active learning that will enable to emulate non-linear hydro-morphodynamic models. The proposed approach accounts for the sparse nature of measurement data and aims to significantly shorten computationally demanding simulations. The pathway to solving the modelling challenges implies the development of (1) a hybrid modelling chain for deterministic modelling; (2) a surrogate emulator based on stochastic approaches and information theory; (3) stochastic routines to leverage model selection, calibration and justification; and (4) a transfer concept to real-world systems for justifiability analysis. This project will boost hydro-morphodynamic modelling to evolve from a subjective deterministic workflow to a sophisticated, stochastically optimized, and objectively transparent sequence of algorithms.

模拟河流生态系统中的水地貌动力学过程面临着通过专家驱动的简化假设来再现复杂、动态和高度可变的系统的挑战。出于这个原因，一个在长时空尺度上再现水地貌动力学的模型，例如用于气候变化分析，涉及巨大的不确定性。建立水地貌动力学模型所需的输入数据涉及有关生态系统特征的信息，如沉积物颗粒大小和表面高程。然而，每个数据集在时间或空间上都有差距，往往具有相当大的不确定性。因此，建模过程涉及一连串的数据采集和处理，以及复杂系统的大量简化，这导致了各种类型的不确定性。建模链中的这些步骤(及其弱点)构成了关于复杂水地貌动力学模型不确定性量化的重大研究挑战。此外，多维水地貌动力学建模概念的选择具有挑战性，因为存在许多不同的建模方法，需要作出合理的决定。因此，水地貌动力学模型可以受益于模型选择、计算和调整的严格和统计方法。为了以可行的计算成本解决这些建模挑战，我们的项目提出了一种基于贝叶斯分析、信息论和主动学习的机器学习方法，该方法将能够模拟非线性水地貌模型。提出的方法考虑了测量数据的稀疏性，并旨在显著缩短计算要求的模拟。解决建模挑战的途径意味着开发(1)用于确定性建模的混合建模链；(2)基于随机方法和信息论的代理仿真器；(3)利用模型选择、校准和证明的随机例程；以及(4)将概念转移到真实世界系统以进行合理性分析。该项目将推动水地貌动力学建模从主观的确定性工作流程演变为复杂的、随机优化的和客观透明的算法序列。