Learning from mistakes-Assessing the performance and uncertainty in process-based models.

ABSTRACT: Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrised or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analysing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (1) training a machine-learning (ML) error model using the input data of the process-based model and other available variables, (2) estimation of l

SUBMITTER: Feigl M 

PROVIDER: S-EPMC9306826 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature