水文科学与工程中机械建模与人工智能的融合

R. Muñoz‐Carpena, Alvaro Carmona-Cabrero, Ziwen Yu, G. Fox, O. Batelaan
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引用次数: 1

摘要

水文学是一门基于第一性原理的成熟的物理科学。然而,水系统是复杂的,其研究需要分析传统和新型遥感和物联网传感器技术中越来越多的可用数据。人工智能(AI)和机器学习(ML)等新的数据驱动方法尽管存在明显的局限性(透明度、可解释性、道德性),但仍吸引了大量“炒作”。一些AI/ML应用程序缺乏明确重要的水文问题,主要关注“黑匣子”预测,而没有提供机械见解。我们根据四种主要类型的水文问题的主要空间和时间尺度,对其进行了分类,回顾了其当前的工具和挑战,并围绕三个主要主题确定了AI/ML在水文领域的重要机遇:数据管理、见解和知识提取,以及建模结构。我们提出,在丰富的水文工具箱中,AI/ML可以是一种强大的归纳和探索性降维工具,而不仅仅是用于预测,以支持新理论的发展,解决不断变化的水文系统中的长期差距。AI/ML可以合并其他形式的结构化和非结构化数据以及基于过程的模型中通常不考虑的传统知识。这可以帮助我们进一步推进对水文系统的基于过程的理解、预测和管理,特别是在具有大模型的更大集成系统规模下。我们呼吁重新构想水文中人工智能的原始定义,不仅要纳入今天对学习的主要关注,还要纳入决策分析和行动规则,以及在强大的道德、法律、社会和经济约束下,在持续的学习和改进循环中开发自主机器。为此,需要在公共部门和私营部门的大力投资下,建立跨学科的知识和实践社区,以在需求加速和环境变化的情况下,将水作为一种共同利益加以保护。
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Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering
Hydrology is a mature physical science based on application of first principles. However, the water system is complex and its study requires analysis of increasingly large data available from conventional and novel remote sensing and IoT sensor technologies. New data-driven approaches like Artificial Intelligence (AI) and Machine Learning (ML) are attracting much “hype” despite their apparent limitations (transparency, interpretability, ethics). Some AI/ML applications lack in addressing explicitly important hydrological questions, focusing mainly on “black-box” prediction without providing mechanistic insights. We present a typology of four main types of hydrological problems based on their dominant space and time scales, review their current tools and challenges, and identify important opportunities for AI/ML in hydrology around three main topics: data management, insights and knowledge extraction, and modelling structure. Instead of just for prediction, we propose that AI/ML can be a powerful inductive and exploratory dimension-reduction tool within the rich hydrological toolchest to support the development of new theories that address standing gaps in changing hydrological systems. AI/ML can incorporate other forms of structured and non-structured data and traditional knowledge typically not considered in process-based models. This can help us further advance process-based understanding, forecasting and management of hydrological systems, particularly at larger integrated system scales with big models. We call for reimagining the original definition of AI in hydrology to incorporate not only today’s main focus on learning, but on decision analytics and action rules, and on development of autonomous machines in a continuous cycle of learning and refinement in the context of strong ethical, legal, social, and economic constrains. For this, transdisciplinary communities of knowledge and practice will need to be forged with strong investment from the public sector and private engagement to protect water as a common good under accelerated demand and environmental change.
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