IDEAS home Printed from https://ideas.repec.org/a/plo/pwat00/0000059.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000059
    Download Restriction: no
    File URL: https://journals.plos.org/water/article/file?id=10.1371/journal.pwat.0000059&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pwat.0000059?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    2. Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    3. Cao, Jason & Tao, Tao, 2025. "Can an identified environmental correlate of car ownership serve as a practical planning tool?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
    4. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    5. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    6. Jeetesh Sharma & Murari Lal Mittal & Gunjan Soni, 2024. "Condition-based maintenance using machine learning and role of interpretability: a review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(4), pages 1345-1360, April.
    7. Hu, Songhua & Xiong, Chenfeng & Ji, Ya & Wu, Xin & Liu, Kailun & Schonfeld, Paul, 2024. "Understanding factors influencing user engagement in incentive-based travel demand management program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    8. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    9. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
    10. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    11. María Cecilia Acevedo & Leandro Andrián & Victoria Nuguer & Oscar Mauricio Valencia, 2023. "Understanding the Rise in Debt," IDB Publications (Book Chapters), in: Andrew Powell & Oscar Mauricio Valencia (ed.), Dealing with Debt, edition 1, chapter 4, pages 67-94, Inter-American Development Bank.
    12. Leopoldo Avellán & Arturo Galindo & Giulia Lotti, 2023. "Official Creditors: Providing More than Money," IDB Publications (Book Chapters), in: Andrew Powell & Oscar Mauricio Valencia (ed.), Dealing with Debt, edition 1, chapter 7, pages 161-182, Inter-American Development Bank.
    13. Xhoxhi, Orjon & Imami, Drini & Hanf, Jon & Gjokaj, Ekrem, 2022. "Too much power or no power: when does intermediary's power result into better wine and happier farmers?," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 25(5), December.
    14. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2023. "Business cycle and realized losses in the consumer credit industry," LIDAM Discussion Papers LFIN 2023007, Université catholique de Louvain, Louvain Finance (LFIN).
    15. Ghosh, Indranil & De, Arijit, 2024. "Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    16. Raffaele Marchi & Alessandro Moro, 2024. "Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models," Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
    17. Moritz Stang & Bastian Krämer & Cathrine Nagl & Wolfgang Schäfers, 2023. "From human business to machine learning—methods for automating real estate appraisals and their practical implications [Vom Vergleichswertverfahren zum maschinellen Lernen – Methoden zur automatisi," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(2), pages 81-108, October.
    18. Felix Lorenz & Jonas Willwersch & Marcelo Cajias & Franz Fuerst, 2023. "Interpretable machine learning for real estate market analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1178-1208, September.
    19. João A. Bastos & Maria Inês Bernardes, 2024. "Understanding online purchases with explainable machine learning," Working Papers REM 2024/0313, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    20. Limon Barua & Bo Zou & Yan Zhou & Yulin Liu, 2023. "Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017," Transportation, Springer, vol. 50(2), pages 437-476, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pwat00:0000059. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: water (email available below). General contact details of provider: https://journals.plos.org/water .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.