TL;DR
An explainable hybrid deep learning framework in which each module targets a specific forecasting challenge provides an accurate, interpretable, and scalable approach for monthly PWV forecasting over Türkiye.
A collaborative forecasting framework that integrates a gated convolutional Transformer (GCTrans) with an improved black kite algorithm (IBKA), enabling accurate, stable, and interpretable daily-scale runoff prediction, and offers a valuable methodological reference for deep learning-based runoff forecasting in complex...
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.
The findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulator...
Experimental results demonstrated that decomposing PWV into linear and nonlinear components via wavelet transform can enhance prediction accuracy and among all the schemes tested, ARIMA-KOA-CNN-GRU-Attention performed the best, achieving a 52% RMSE reduction in comparison with the ARIMA-KOA-GRU method.
A hybrid Convolutional-Transformer forecasting framework that combines convolutional encoding for spatial feature extraction with factorised self-attention for spatio-temporal dependency modelling and introduces two seasonal prior mechanisms.
Deep learning’s significant potential as a scalable and dependable method for regional climate forecasting, giving it an advantage in the Kathmandu Valley’s climate change adaptation policy-making process is supported.
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