Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion maps, and the model complexity. The current Spatially-Explicit Uncertainty and Sensitivity Analysis (SEUSA) approach employs a cluster-based parallel and distributed Python–Dask solution for large-scale spatial problems, which validates and quantifies the robustness of spatial model solutions. This paper presents the design of a framework to perform SEUSA as a Service in a cloud-based environment scalable to very large raster datasets and applicable to various domains, such as landscape assessment, site selection, risk assessment, and land-use management. It incorporates an automated Kubernetes service for container virtualization, comprising a set of microservices to perform SEUSA as a Service. Implementing the proposed framework will contribute to a more robust assessment of spatial multi-criteria decision-making applications, facilitating a broader access to SEUSA by the research community and, consequently, leading to higher quality decision analysis.

中文翻译：

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多标准模型中基于云的空间显式不确定性和敏感性分析的框架

全局敏感度分析（例如用于大规模栅格数据集的基于方差的方法）在计算上特别昂贵且占用大量内存，从而限制了其在商品集群计算中的适用性。计算工作量主要取决于模型运行的数量，空间，光谱和时间分辨率，标准图的数量以及模型的复杂性。当前的空间显式不确定性和灵敏度分析（SEUSA）方法采用基于群集的并行和分布式Python–Dask解决方案来解决大规模空间问题，从而验证并量化了空间模型解决方案的鲁棒性。本文介绍了在基于云的环境中执行SEUSA即服务的框架设计，该环境可扩展到非常大的栅格数据集并适用于各种领域，例如景观评估，选址，风险评估和土地使用管理。它并入了用于容器虚拟化的自动化Kubernetes服务，其中包括一组微服务以执行SEUSA即服务。实施建议的框架将有助于对空间多准则决策应用程序进行更健壮的评估，从而促进研究团体更广泛地访问SEUSA，从而导致更高质量的决策分析。