基于自动机器学习分类的地球科学模型动态加权集成

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscientific Model Development Pub Date : 2023-10-12 DOI:10.5194/gmd-16-5685-2023
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, Xi Chen
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引用次数: 0

摘要

摘要尽管地球科学(例如,物理或数据驱动)模型最近有所发展,但在地球科学领域的许多分支学科中,有效地组装多个模型以接近基准解决方案仍然具有挑战性。在这里,我们提出了一个自动机器学习辅助集成框架(AutoML-Ens),试图解决这一挑战。详细介绍了AutoML-Ens的方法和工作流程,并利用机器学习分类器得到的概率与分配给候选集成成员的动态权重之间的映射这一关键策略实现了原型模型。在此基础上,探讨了该框架在全球土壤保水参数制图和遥感农田蒸散估算两个实例中的应用。结果表明,与传统的集成方法相比,AutoML-Ens在数据集(训练、测试和整体数据集)和环境梯度上都具有优势,性能指标(如决定系数、克林-古普塔效率和均方根误差)也有所改善。由于AutoML-Ens具有两个独特的特性,即为候选模型分配动态权重和充分利用automl辅助工作流,因此更好的性能表明AutoML-Ens在改进量化和减少估计中的不确定性方面具有巨大的潜力。除了具有代表性的结果外,我们还讨论了所使用框架的解释方面及其可能的扩展。更重要的是,我们强调了将数据驱动方法与物理约束结合起来解决具有高维空间和非线性行为的地球科学模型集成问题的好处。
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Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Abstract. Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature.
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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