Artificial intelligence (AI) methods have created insurmountable performance in prediction tasks for geoscientific problems yet are unable to derive process insights and answer specific scientific questions. The geoscience community faces a dilemma of reconciling process comprehension with high predictive accuracy. Here we introduce a deep process learning (DPL) approach empowering neural networks to deduce intrinsic processes from observable data, wherein the intuitive physics of geosystems is directly coupled within the deep learning (DL) architecture as structural prior. We aim to incorporate as raw common concepts as possible as macroscopic guidance: on the one hand, to reduce interference with DL's data adaptability. On the other hand, to allow the information flow of the model to converge along specific paths toward the target output, thus enabling the potential to gain process insights with limited supervision. Illustrating its application to precipitation-runoff modeling across the USA, DPL yields an ensemble median Nash-Sutcliffe efficiency of 0.758 and Kling-Gupta efficiency of 0.778 with robust transferability, compared to 0.762 and 0.751 for the state-of-the-art DL model. The good match between internal representations of DPL and independent data sets of snow water equivalent and evapotranspiration, along with its superior capability for catchment water budget closures, demonstrates proficient process mastery. The study also highlights beneficial synergies from large-scale data collaboration, promoting the organic unity of process understanding and predictive performance. This work shows a promising avenue for learning processes from big data and will benefit geoscientific domains that remain concerned with process clarity in the era of AI.

中文翻译：

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在深度学习中协同直观物理和大数据：我们能否在保持最先进的水文预测能力的同时获得过程洞察？


人工智能 （AI） 方法在地球科学问题的预测任务中创造了难以逾越的性能，但无法获得过程见解和回答特定的科学问题。地球科学界面临着一个两难境地，即既要协调过程理解又要实现高预测精度。在这里，我们介绍了一种深度过程学习 （DPL） 方法，使神经网络能够从可观察数据中推断出内在过程，其中几何系统的直观物理学作为结构先验直接耦合在深度学习 （DL） 架构中。我们的目标是尽可能地将原始的通用概念作为宏观指导：一方面，减少对 DL 数据适应性的干扰。另一方面，允许模型的信息流沿着特定路径收敛到目标输出，从而有可能在有限的监督下获得过程洞察力。为了说明其在美国降水径流建模中的应用，DPL 产生的集合中位数 Nash-Sutcliffe 效率为 0.758，Kling-Gupta 效率为 0.778，具有强大的可传递性，而最先进的 DL 模型的 0.762 和 0.751。DPL 的内部表示与雪水当量和蒸散的独立数据集之间的良好匹配，以及其卓越的集水区水收支关闭能力，表明了熟练的工艺掌握。该研究还强调了大规模数据协作的有益协同作用，促进了过程理解和预测性能的有机统一。这项工作展示了一条从大数据中学习过程的有前途的途径，并将使在 AI 时代仍然关注过程清晰度的地球科学领域受益。