IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3793-d942611.html
   My bibliography  Save this article

Survey on the Application of Artificial Intelligence in ENSO Forecasting

Author

Listed:
  • Wei Fang

    (School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
    Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yu Sha

    (School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Victor S. Sheng

    (Department of Computer, Texas Tech University, Lubbock, TX 79409, USA)

Abstract

Climate disasters such as floods and droughts often bring heavy losses to human life, national economy, and public safety. El Niño/Southern Oscillation (ENSO) is one of the most important inter-annual climate signals in the tropics and has a global impact on atmospheric circulation and precipitation. To address the impact of climate change, accurate ENSO forecasts can help prevent related climate disasters. Traditional prediction methods mainly include statistical methods and dynamic methods. However, due to the variability and diversity of the temporal and spatial evolution of ENSO, traditional methods still have great uncertainty in predicting ENSO. In recent years, with the rapid development of artificial intelligence technology, it has gradually penetrated into all aspects of people’s lives, and the climate field has also benefited. For example, deep learning methods in artificial intelligence can automatically learn and train from a large amount of sample data, obtain excellent feature representation, and effectively improve the performance of various learning tasks. It is widely used in computer vision, natural language processing, and other fields. In 2019, Ham et al. used a convolutional neural network (CNN) model in ENSO forecasting 18 months in advance, and the winter ENSO forecasting skill could reach 0.64, far exceeding the dynamic model with a forecasting skill of 0.5. The research results were regarded as the pioneering work of deep learning in the field of weather forecasting. This paper introduces the traditional ENSO forecasting methods and focuses on summarizing the various latest artificial intelligence methods and their forecasting effects for ENSO forecasting, so as to provide useful reference for future research by researchers.

Suggested Citation

  • Wei Fang & Yu Sha & Victor S. Sheng, 2022. "Survey on the Application of Artificial Intelligence in ENSO Forecasting," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3793-:d:942611
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3793/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/20/3793/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yoo-Geun Ham & Jeong-Hwan Kim & Jing-Jia Luo, 2019. "Deep learning for multi-year ENSO forecasts," Nature, Nature, vol. 573(7775), pages 568-572, September.
    2. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    3. So, Mike K.P. & Chung, Ray S.W., 2014. "Dynamic seasonality in time series," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 212-226.
    4. F. Siegert & G. Ruecker & A. Hinrichs & A. A. Hoffmann, 2001. "Increased damage from fires in logged forests during droughts caused by El Niño," Nature, Nature, vol. 414(6862), pages 437-440, November.
    5. Jonathan A. Patz & Diarmid Campbell-Lendrum & Tracey Holloway & Jonathan A. Foley, 2005. "Impact of regional climate change on human health," Nature, Nature, vol. 438(7066), pages 310-317, November.
    6. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    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. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.

    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. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    2. Simona Mikšíková & David Ulčák & František Kuda, 2022. "Analysis of Malfunctions in Selected Parking Systems in the Czech Republic," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    3. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    4. Hossein Yousefi & Mohammad Hasan Ghodusinejad & Armin Ghodrati, 2022. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran," Energies, MDPI, vol. 15(24), pages 1-25, December.
    5. Dyna Heng & Anna Ivanova & Rodrigo Mariscal & Ms. Uma Ramakrishnan & Joyce Wong, 2016. "Advancing Financial Development in Latin America and the Caribbean," IMF Working Papers 2016/081, International Monetary Fund.
    6. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    7. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    8. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    9. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    10. Ahmed Belhadjayed & Grégoire Loeper & Frédéric Abergel, 2016. "Forecasting Trends With Asset Prices," Post-Print hal-01512431, HAL.
    11. Karzan Mahdi Ghafour & Abdulqadir Rahomee Ahmed Aljanabi, 2023. "The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective," Operations Management Research, Springer, vol. 16(2), pages 780-793, June.
    12. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    13. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    14. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    15. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    16. Kosuke Kawakami & Hirokazu Kobayashi & Kazuhide Nakata, 2021. "Seasonal Inventory Management Model for Raw Materials in Steel Industry," Interfaces, INFORMS, vol. 51(4), pages 312-324, July.
    17. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    18. Xianbo Li, 2022. "Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    19. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    20. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.

    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:gam:jmathe:v:10:y:2022:i:20:p:3793-:d:942611. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.