用时空变压器预测全国核辐射，解决监测点不均与波动难测问题
NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer
- 设计非平稳时间注意力+不平衡空间注意力+辐射传播提示模块
- 在两个真实数据集上超越11个基线模型，提升预测精度
- 适合关注环境安全、灾害预警的科研与政策制定者
核辐射是原子核衰变时释放的能量，对人类健康和环境安全构成重大威胁。近年来，监测技术进步使核辐射水平及相关因素（如气象条件）得以有效记录。海量监测数据为构建精准可靠的核辐射预测模型提供了可能，对个人与政府决策至关重要。然而，由于监测站点在广阔地理范围内分布不均，且辐射变化模式具有非平稳性，该任务极具挑战。本文提出NRFormer，一种面向全国核辐射变化预测的新框架。通过整合非平稳时间注意力模块、不平衡感知空间注意力模块以及辐射传播提示模块，共同捕捉核辐射复杂的时空动态。在两个真实数据集上的大量实验表明，所提框架显著优于11种基线模型。
原文摘要 · Abstract (English)
Nuclear radiation, which refers to the energy emitted from atomic nuclei during decay, poses significant risks to human health and environmental safety. Recently, advancements in monitoring technology have facilitated the effective recording of nuclear radiation levels and related factors, such as weather conditions. The abundance of monitoring data enables the development of accurate and reliable nuclear radiation forecasting models, which play a crucial role in informing decision-making for individuals and governments. However, this task is challenging due to the imbalanced distribution of monitoring stations over a wide spatial range and the non-stationary radiation variation patterns. In this study, we introduce NRFormer, a novel framework tailored for the nationwide prediction of nuclear radiation variations. By integrating a non-stationary temporal attention module, an imbalance-aware spatial attention module, and a radiation propagation prompting module, NRFormer collectively captures complex spatio-temporal dynamics of nuclear radiation. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed framework against 11 baselines.
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