Aug 2026· International Conference on Machine Vision and Deep Learning· Vol 14326, pp. 143260B - 143260B-6· 0 citations· 22 references
Engineering
TL;DR
TCN-Attention is proposed, integrating Temporal Convolutional Networks with multi-head self-attention for weekly streamflow prediction, employing dilated causal convolutions to capture multi-scale temporal patterns, while the self-attention module dynamically identifies informative time steps.
Abstract
Accurate streamflow prediction is essential for water resource management and hydropower scheduling. Traditional RNNs (LSTM, GRU) suffer from sequential processing limitations in capturing long-range temporal dependencies. We propose TCN-Attention, integrating Temporal Convolutional Networks with multi-head self-attention for weekly streamflow prediction. The TCN employs dilated causal convolutions to capture multi-scale temporal patterns, while the self-attention module dynamically identifies informative time steps. Evaluated on the CCAM HydroMLYR dataset (102 Yangtze River catchments, 11 meteorological features, 12 static attributes), TCN-Attention achieves NSE of 0.2016 and RMSE of 0.9881, outperforming LSTM (NSE=0.1869), Transformer (NSE=0.1738), and GRU (NSE=-0.1044) baselines.
Nowadays, traffic congestion significantly contributes to severe economic and environmental problems in cities. Anticipating traffic flow is a highly effective strategy for mitigating congestion, and accurate predictions are vital for practical traffic guidance, management, and control. However, many existing approache...
Negin Bagherzadeh Rezaie, H. Shirmohammadi, Z. Shirmohammadi et al.· Scientific Reports· 0 citations
Compared with single-task and multi-task TCN baselines, the proposed MTC-TCN-Attn model consistently reduces prediction errors and improves robustness by effectively integrating temporal dependency modeling and cross-task information sharing.
Wei-Guang Ning, Ji-Wei Gao, Tao Han et al.· Italian National Conference...· 0 citations
Distributed sensor networks for accurate short-range weather forecasting are difficult because of measurement noise, missing measurements, and complex spatio-temporal dependencies. In this research, an integrated four-stage deep learning system to robustly predict weather is introduced. First, a Variational Autoencoder...
Air temperature is a critical meteorological parameter that dynamically affects density altitude and Maximum Take-Off Weight (MTOW) calculations in aviation operations. Although the 10-minute resolution Automatic Weather Station (AWS) data at Soekarno-Hatta International Airport effectively captures micrometeorological...
Muhamad Nur Fuat, S. Anggai, Murni Handayani· JOURNAL OF APPLIED INFORMATI...· 0 citations
ST-ResFormer is proposed, a spatiotemporal transformer architecture that fuses a graph-attention spatial encoder with a patch-based sparse-attention temporal encoder through a cross-attention fusion block, enabling joint multi-horizon forecasting of three coupled resource variables from heterogeneous data sources.
Lalita· Natural Resources for Human...· 0 citations
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