The proposed dual-branch encoder-decoder predicts global gross primary productivity from climate data with RMSE 0.836, outperforming LightGBM (1.063) and TabNet (0.944) on a 2001-2020 dataset.
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An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity
The proposed dual-branch encoder-decoder predicts global gross primary productivity from climate data with RMSE 0.836, outperforming LightGBM (1.063) and TabNet (0.944) on a 2001-2020 dataset.