HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
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DOI：
10.5194/hess-22-5639-2018
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发表时间：
2018-11-01
影响因子：
6.3
通讯作者：
Tsai, Wen-Ping
中科院分区：
文献类型：
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作者：
Shen, Chaopeng;Laloy, Eric;Tsai, Wen-Ping
最近，深度学习（DL）已经成为一种革命性的多功能工具，改变了行业应用，并为科学发现和模型构建提供了新的和改进的能力。到目前为止，在水文学中采用DL是渐进的，但该领域现在已经成熟，可以取得突破。本文认为，基于dll的方法可以为水文科学的知识发现开辟一条互补的途径。在新的途径中，机器学习算法提出了与数据一致的竞争性假设。然后调用疑问方法来解释DL模型，以供科学家进一步评估。然而，水文学对深度学习方法提出了许多挑战，如数据限制、异质性和协同演化，以及水文学领域对深度学习的普遍缺乏经验。实现由人工智能驱动的科学进步的路线图需要一个包括科学家和公民在内的大型社区的协调努力。将基于过程的模型与深度学习模型集成将有助于减轻数据限制。数据和基线模型的共享将提高整个社区的效率。公开比赛可以作为组织活动，极大地推动增长，培养水文数据科学教育，这需要基层合作。水文深度学习领域提供了许多研究机会，反过来也可以刺激机器学习的进步。
Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DLbased methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.