IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i10d10.1007_s11269-022-03240-y.html
   My bibliography  Save this article

A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology

Author

Listed:
  • Mohamed Hamitouche

    (Salamanca University, High Polytechnic School of Engineering Avila)

  • Jose-Luis Molina

    (Salamanca University, High Polytechnic School of Engineering Avila)

Abstract

Forecasting systems for foreseeing water levels and flow rates have become necessary to mitigate climate change negative impacts. Most of these systems are based on powerful tools such as Artificial Intelligence (AI) methods. This paper presents a comprehensive review of AI methods for high-flow extremes prediction. The review starts with an overview of the state-of-the-art AI techniques and examples of their application, followed by a SWOT analysis to benchmark their predictive capability based on set of criteria. Finally, the most suitable AI methods for short-term and/or long-term prediction, based on a rigorous suitability assessment are proposed. As a result, Fourteen AI methods have been identified. Their evaluation revealed that the methods that averagely behave the best for achieving high-flow extremes prediction are ANNs, SVMs, wavelets and Bayesian methods, at all-time scales. The latter, as stochastic methods, have the privilege by their cheap computation cost, their reliability and ability to handle hydrological uncertainty, and their capacity to perform causal relationships between features. This study also urges researchers to further explore the predictive potential of decision trees, ensembles, CNNs, MARS, GP and agent-based methods for high-flow extremes.

Suggested Citation

  • Mohamed Hamitouche & Jose-Luis Molina, 2022. "A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3859-3876, August.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03240-y
    DOI: 10.1007/s11269-022-03240-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03240-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03240-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rashad Aliyev & Sara Salehi & Rafig Aliyev, 2019. "Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting," Sustainability, MDPI, vol. 11(3), pages 1-13, February.
    2. Fiaz Hussain & Ray-Shyan Wu & Jing-Xue Wang, 2021. "Comparative study of very short-term flood forecasting using physics-based numerical model and data-driven prediction model," 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. 107(1), pages 249-284, May.
    3. Yiheng Li & Weidong Chen, 2020. "A Comparative Performance Assessment of Ensemble Learning for Credit Scoring," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    4. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.
    2. Jingwei Huang & Hui Qin & Yongchuan Zhang & Dongkai Hou & Sipeng Zhu & Pingan Ren, 2023. "Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4475-4490, September.

    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. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    2. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    3. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    4. Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.
    5. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    6. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
    7. Babek Erdebilli & Burcu Devrim-İçtenbaş, 2022. "Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    8. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
    9. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    10. Guner Altan & Server Demirci, 2022. "Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 397-424, July.
    11. Renata Graf & Viktor Vyshnevskyi, 2022. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions," Resources, MDPI, vol. 11(12), pages 1-24, November.

    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:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03240-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.