Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computational efficiency. Machine intelligence, especially deep learning, promises a paradigm shift in overcoming these challenges. A critical examination of existing machine-driven methods reveals the inherent limitations, particularly in terms of the interpretability and the ability to generalize findings. To overcome these challenges, we develop a ternary framework that synergizes the valuable insights from multisource data, human expertise, and machine intelligence. This framework capitalizes on the distinct strengths of each element: the value and relevance of multisource data, the innovative capacity of human expertise, and the analytical efficiency of machine intelligence. Our goal is to conceptualize sustainable water management practices and enhance our understanding and predictive capabilities of groundwater systems. Unlike approaches that rely solely on abundant data, our framework emphasizes the quality and strategic use of available data, combined with human intellect and advanced computing, to overcome current limitations and pave the way for more realistic groundwater simulations.

中文翻译：

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通过多源数据、人类专业知识和机器智能的三元框架概念化未来地下水模型


地下水模型对于理解含水层系统行为和有效水资源时空分布至关重要，但它们常常受到与模型假设、参数化、不确定性和计算效率相关的挑战的阻碍。机器智能，尤其是深度学习，有望在克服这些挑战方面实现范式转变。对现有机器驱动方法的严格检查揭示了其固有的局限性，特别是在可解释性和概括研究结果的能力方面。为了克服这些挑战，我们开发了一个三元框架，可以整合来自多源数据、人类专业知识和机器智能的宝贵见解。该框架利用了每个元素的独特优势：多源数据的价值和相关性、人类专业知识的创新能力以及机器智能的分析效率。我们的目标是概念化可持续水管理实践，并增强我们对地下水系统的理解和预测能力。与仅仅依赖于丰富数据的方法不同，我们的框架强调可用数据的质量和战略性使用，与人类智慧和先进计算相结合，以克服当前的局限性，并为更真实的地下水模拟铺平道路。