发表机构
The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology; Shandong University（香港科技大学（广州）; 香港科技大学; 山东大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对核辐射预测难题，引入NRFormer+时空变换器，结合非平稳时间注意力、密度自适应空间注意力与大气扩散模块，能估计气象对辐射扩散的影响并注入网络，在多基线数据集上实现高精度预测，降低突发变化平均绝对误差。
AI 中文摘要
核辐射在原子衰变过程中释放能量，对公众健康和环境构成持续风险。现代监测网络记录了数千个站点的辐射水平及天气状况，为全国范围的预测提供了可能。但将这些丰富的监测数据转化为可靠预测存在困难，原因包括时间序列高度非平稳、监测站空间分布不均、辐射与异质环境共同演变。本研究引入NRFormer+，一种用于全国核辐射预测的时空变换器。它将非平稳时间注意力和密度自适应空间注意力与新的大气扩散模块相结合，该模块估计气象如何驱动辐射扩散并将此物理信号作为架构先验注入网络。NRFormer+在所有13个基线的两个数据集上都实现了最先进的准确性，在可比推理延迟下，与最强基线相比，突发变化平均绝对误差最多降低19.1%。代码和数据集可公开获取。
英文摘要
Nuclear radiation, the energy released during atomic decay, poses persistent risks to public health and the environment, and concerns have only grown since the Fukushima accident and the recent commencement of treated-water discharge. Modern monitoring networks now record radiation levels and accompanying weather conditions at thousands of stations, opening the door to nationwide forecasting that can inform emergency response, agricultural advisories, and routine public-safety decisions. However, turning this abundance of monitoring data into reliable forecasts is difficult for three reasons. First, the time series at each station are highly non-stationary, shaped by radioactive decay, weather variability, and irregular human interventions. Second, monitoring stations are severely unevenly distributed in space. Roughly 78% of Japan's stations sit in less than 6% of the country, clustered near Fukushima, which breaks the assumptions of standard graph-based models. Third, radiation co-evolves with heterogeneous context such as wind, temperature, and humidity through atmospheric transport processes that purely data-driven models struggle to capture from observations alone. In this study, we introduce NRFormer+, a spatio-temporal Transformer for nationwide nuclear radiation forecasting. NRFormer+ couples non-stationary temporal attention and density-adaptive spatial attention with a new atmospheric diffusion module that estimates how meteorology drives radiation dispersion and injects this physical signal into the network as an architectural prior. NRFormer+ delivers state-of-the-art accuracy on both datasets across all 13 baselines, reducing sudden-change MAE by up to 19.1% over the strongest baseline at comparable inference latency. Our code and datasets are publicly available at https://github.com/tfeilyu/NRFormer_Plus.
