Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to maximise measurement informativeness and place sensors efficiently, particularly in remote regions like Antarctica. Probabilistic machine learning models can evaluate placement informativeness by predicting the uncertainty reduction provided by a new sensor. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as ground truth, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future work towards an operational sensor placement recommendation system. This system could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.
翻译:环境传感器对于监测气象条件以及气候变化的影响至关重要。然而,在像南极这样的偏远地区,最大化测量信息和高效放置传感器都是具有挑战性的。概率机器学习模型可以通过预测新传感器提供的不确定性减少来评估放置信息,高斯过程(GP)模型是其常用方法之一,但是它们难以捕捉到复杂的非平稳行为,并且难以管理大数据集。本文提出使用卷积高斯神经过程(ConvGNP)来解决这些问题。ConvGNP使用神经网络来对任意目标位置进行高斯联合分布参数化,从而实现了灵活性和可扩展性。使用南极模拟表面空气温度异常作为基础真相,ConvGNP学习了空间和季节性的非平稳性,相较于非平稳GP基线表现更优。在模拟的传感器放置实验中,ConvGNP更好地预测了新观测带来的性能提升,比GP基线有更好的表现,从而实现了更具信息性的传感器放置。我们将这种方法与基于物理的传感器放置方法进行对比,并提出未来工作以建立一个操作性的传感器放置推荐系统。这个系统可以用于实现环境数字孪生体,以实时引导测量采样,以提高数字现实的表达能力。