시공간 그래프 신경망을 활용한 댐 유입량 예측에서의 공간 정보 기여도 평가: 충주댐 및 소양강댐 비교

초록

Accurate prediction of dam inflow is essential for flood control and water resources management. This study quantitatively evaluated the contribution of spatial information in dam inflow prediction using Spatio-Temporal Graph Neural Networks (ST-GNN). ST-GNN and five baseline models (LSTM, GRU, TCN, Transformer, LSTM-Attention) were compared for Chungju Dam (6,648 km2 ) and Soyanggang Dam (2,703 km2 ) across five lead times (T+3h to T+48h). An ablation study quantified spatial information contribution by computing ΔNSE between ST-GNN with and without graph convolution layers. For Chungju Dam, ΔNSE turned positive from T+12h, reaching +0.028 at T+24h, with block bootstrap analysis confirming statistical significance (95% CI: [+0.010, +0.111]). In contrast, Soyanggang Dam showed negative ΔNSE across all lead times (T+24h:-0.035), indicating that graph-based spatial aggregation degraded performance in a steep, single-channel basin. These results demonstrate that the effectiveness of graph-based spatial information is basin-dependent, governed by stream network complexity and hydrological response characteristics, providing quantitative evidence for basin-specific model selection strategies.

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