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학술지 Spatiotemporal neural network with attention mechanism for El Nino forecasts
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김진아, 권민호, 김성대, 국종성, 유준규, 김재일
Scientific Reports, v.12, pp.1-15
Nature Research
22HH3800, 저궤도 초소형위성(10kg 급) 기반 글로벌 IoT 서비스를 위한 저전력 위성다중액세스 핵심기술개발, 유준규
To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Ni챰o predictions for long-lead forecasts. The STANet makes two critical architectural improvements: it learns spatial features globally by expanding the network's receptive field and encodes long-term sequential features with visual attention using a stateful long-short term memory network. The STANet conducts multitask learning of Nino3.4 index prediction and calendar month classification for predicted indices. In a comparison of the proposed STANet performance with the state-of-the-art model, the accuracy of the 12-month forecast lead correlation coefficient was improved by 5.8% and 13% for Nino3.4 index prediction and corresponding temporal classification, respectively. Furthermore, the spatially attentive regions for the strong El혻Ni챰o events displayed spatial relationships consistent with the revealed precursor for El혻Ni챰o occurrence, indicating that the proposed STANet provides good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content for El혻Ni챰o evolution.
KSP 제안 키워드
Attention mechanism, Correlation Coefficient, El Nino, Geophysical Data, Heat content, Long-short term memory(LSTM), Memory network, Receptive field, Sea Surface Temperature, Sequential features, Spatial relationships
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