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Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering

Author

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  • Rafael Muñoz-Carpena
  • Alvaro Carmona-Cabrero
  • Ziwen Yu
  • Garey Fox
  • Okke Batelaan

Abstract

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.

Suggested Citation

  • Rafael Muñoz-Carpena & Alvaro Carmona-Cabrero & Ziwen Yu & Garey Fox & Okke Batelaan, 2023. "Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering," PLOS Water, Public Library of Science, vol. 2(8), pages 1-24, August.
    • Handle: RePEc:plo:pwat00:0000059
    DOI: 10.1371/journal.pwat.0000059
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    References listed on IDEAS

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    1. Jianzhuo Yan & Tiantian Lv & Yongchuan Yu, 2018. "Construction and Recommendation of a Water Affair Knowledge Graph," Sustainability, MDPI, vol. 10(10), pages 1-15, September.
    2. Achraf Tounsi & Marouane Temimi, 2023. "A systematic review of natural language processing applications for hydrometeorological hazards assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2819-2870, April.
    3. Rana Muhammad Adnan & Andrea Petroselli & Salim Heddam & Celso Augusto Guimarães Santos & Ozgur Kisi, 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2987-3011, February.
    4. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
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