Deploying environmental measurement stations can be a costly and time-consuming procedure, especially in remote regions that are difficult to access, such as Antarctica. Therefore, it is crucial that sensors are placed as efficiently as possible, maximising the informativeness of their measurements. This can be tackled by fitting a probabilistic model to existing data and identifying placements that would maximally reduce the model's uncertainty. The models most widely used for this purpose are Gaussian processes (GPs). However, designing a GP covariance which captures the complex behaviour of non-stationary spatiotemporal data is a difficult task. Further, the computational cost of GPs makes them challenging to scale to large environmental datasets. In this work, we explore using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP is a meta-learning model that uses neural networks to parameterise a GP predictive. Our model is data-driven, flexible, efficient, and permits multiple input predictors of gridded or scattered modalities. Using simulated surface air temperature fields over Antarctica as ground truth, we show that a ConvGNP significantly outperforms a non-stationary GP baseline in terms of predictive performance. We then use the ConvGNP in an Antarctic sensor placement toy experiment, yielding promising results.
翻译:部署环境测量台站可能是一项昂贵和耗时的程序,特别是在南极洲等难以进入的偏远地区。因此,至关重要的是,传感器应尽量高效地放置传感器,使其测量信息量达到最大程度。通过将概率模型与现有数据相匹配,并查明将最大限度地减少模型不确定性的位置,可以解决这个问题。最广泛用于此目的的模型是Gaussian进程。然而,设计一个GP常识,记录非静止随机数据的复杂性行为是一项困难的任务。此外,GP的计算成本使其在规模上对大型环境数据集构成挑战。在这项工作中,我们探索如何使用变动高斯神经程序来解决这些问题。ConPONP是一个元学习模型,使用神经网络来为GP预测参数。我们的模型是数据驱动、灵活、高效和允许电网化或分散模式的多重输入预测器。在利用南极洲表面模拟空气温度场作为地面GPS,我们用GNFS模型的模型来大幅预测GFS的不具有前景。我们用GNFA模型来预测一个具有前景的模型。