NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer

TL;DR

NRFormer is a novel spatio-temporal transformer-based framework designed for nationwide nuclear radiation forecasting, effectively capturing complex dynamics despite data imbalance and non-stationarity.

Contribution

This paper introduces NRFormer, a new model integrating specialized attention modules for improved nationwide nuclear radiation prediction.

Findings

Outperforms 11 baseline models in real-world datasets

Effectively handles data imbalance and non-stationary patterns

Demonstrates superior accuracy in nuclear radiation forecasting

Abstract

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.