融合数据与领域规则，提升空间插值精度和不确定性量化。
A Hybrid Framework for Spatial Interpolation: Merging Data-driven with Domain Knowledge
- 用数据驱动提取空间依赖特征，结合规则映射增强领域知识。
- 在两个场景中表现更优，能捕捉更局部的空间分布特征。
- 支持非线性估计与不确定性量化，适合地理、气象等复杂场景。
通过散点观测数据进行空间分布信息估计时，常忽略领域知识在理解空间依赖关系中的关键作用。此外，这些数据集的特征通常仅限于观测点的空间坐标。本文提出一种混合框架，将数据驱动的空间依赖特征提取与规则辅助的空间依赖函数映射相结合，以增强领域知识。我们在两个对比应用场景中验证了该框架的优越性能，表明其能更好地捕捉重建场中的局部空间特征。此外，我们强调了通过变换模糊规则提升非线性估计能力的潜力，并可量化观测数据集固有的不确定性。该框架通过协同融合观测数据与规则辅助的领域知识，为空间信息估计提供了创新方法。
原文摘要 · Abstract (English)
Estimating spatially distributed information through the interpolation of scattered observation datasets often overlooks the critical role of domain knowledge in understanding spatial dependencies. Additionally, the features of these data sets are typically limited to the spatial coordinates of the scattered observation locations. In this paper, we propose a hybrid framework that integrates data-driven spatial dependency feature extraction with rule-assisted spatial dependency function mapping to augment domain knowledge. We demonstrate the superior performance of our framework in two comparative application scenarios, highlighting its ability to capture more localized spatial features in the reconstructed distribution fields. Furthermore, we underscore its potential to enhance nonlinear estimation capabilities through the application of transformed fuzzy rules and to quantify the inherent uncertainties associated with the observation data sets. Our framework introduces an innovative approach to spatial information estimation by synergistically combining observational data with rule-assisted domain knowledge.
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