Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction

Adaptive period decomposition network with muti-scale temporal variation learning for water level prediction

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Study region:: Hanjiang River Basin, China Study focus:: This study introduces DLGNet, an advanced water level prediction model that employs an adaptive multi-resolution decomposition to decouple multiple periodic components.The model incorporates a 2D overlap patch attention mechanism and separable dilation causal convolution to effectively capture local and global temporal variations.Additionally, DLGNet utilizes multiscale 2D separable convolutions to capture temporal variations among period components and employs a here novel aggregation strategy for feature interaction analysis, ultimately enhancing prediction accuracy through direct decoding.New hydrologic insights for the region:: Compared to ten other advanced prediction models, DLGNet achieved an average reduction in mean square error (MSE) by 94.

82%, 93.35%, and 83.13% for short-term, mid-term, and long-term grand love red heart reposado tequila predictions, respectively.The adaptive period decomposition capability and multiscale temporal variation representation learning of DLGNet ensure its robustness and accuracy across various prediction tasks.

Moreover, in all prediction tasks, the output of DLGNet more closely approximates the ground truth, exhibiting rich local detail changes and aligning more accurately with the actual fluctuations of water levels over time.