线性回归在预测陆地水储量方面仍优于复杂深度模型，值得警惕过度依赖深度学习。
Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage
- 用线性回归对比复杂模型，验证其在多源数据下的稳健性
- 在HydroGlobe数据集上，线性模型对陆地水储量预测精度更高
- 适合关注模型可解释性与基准评估的研究者参考
近年来，长短期记忆网络（LSTM）和Transformer等深度学习模型被广泛应用于水文领域，在多项任务中表现优异，甚至超越物理模型。然而，这些模型在预测受自然变化与人类活动共同影响的陆地水储量（TWS）时是否真正更优仍不明确。本文基于开源、全球代表性的HydroGlobe数据集——包含仅由陆面模型模拟生成的基线版本及融合多源遥感数据同化的进阶版本——发现线性回归作为基准模型，在TWS预测中表现优于LSTM和时序融合变换器（Temporal Fusion Transformer）。研究强调，在开发和评估深度学习模型时，应纳入传统统计模型作为参照。同时，亟需建立能反映自然波动与人类干预综合影响的全球代表性基准数据集。
原文摘要 · Abstract (English)
Recent advances in machine learning such as Long Short-Term Memory (LSTM) models and Transformers have been widely adopted in hydrological applications, demonstrating impressive performance amongst deep learning models and outperforming physical models in various tasks. However, their superiority in predicting land surface states such as terrestrial water storage (TWS) that are dominated by many factors such as natural variability and human driven modifications remains unclear. Here, using the open-access, globally representative HydroGlobe dataset - comprising a baseline version derived solely from a land surface model simulation and an advanced version incorporating multi-source remote sensing data assimilation - we show that linear regression is a robust benchmark, outperforming the more complex LSTM and Temporal Fusion Transformer for TWS prediction. Our findings highlight the importance of including traditional statistical models as benchmarks when developing and evaluating deep learning models. Additionally, we emphasize the critical need to establish globally representative benchmark datasets that capture the combined impact of natural variability and human interventions.
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