IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i16p2447-d1451452.html
   My bibliography  Save this article

An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data

Author

Listed:
  • Annisa Nur Falah

    (Post Doctoral Program, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Yudhie Andriyana

    (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Budi Nurani Ruchjana

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Eddy Hermawan

    (Research Center for Climate and Atmosphere, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia)

  • Teguh Harjana

    (Research Center for Climate and Atmosphere, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia)

  • Edy Maryadi

    (Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency (BRIN), Bandung 40135, Indonesia)

  • Risyanto

    (Research Center for Climate and Atmosphere, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia)

  • Haries Satyawardhana

    (Research Center for Climate and Atmosphere, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia)

  • Sinta Berliana Sipayung

    (Research Center for Climate and Atmosphere, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia)

Abstract

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.

Suggested Citation

  • Annisa Nur Falah & Yudhie Andriyana & Budi Nurani Ruchjana & Eddy Hermawan & Teguh Harjana & Edy Maryadi & Risyanto & Haries Satyawardhana & Sinta Berliana Sipayung, 2024. "An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data," Mathematics, MDPI, vol. 12(16), pages 1-21, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2447-:d:1451452
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/16/2447/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/16/2447/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Annisa Nur Falah & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Juli Rejito, 2023. "The Hybrid Modeling of Spatial Autoregressive Exogenous Using Casetti’s Model Approach for the Prediction of Rainfall," Mathematics, MDPI, vol. 11(17), pages 1-21, 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. Yudhie Andriyana & Annisa Nur Falah & Budi Nurani Ruchjana & Albertus Sulaiman & Eddy Hermawan & Teguh Harjana & Daisy Lou Lim-Polestico, 2024. "Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia," Mathematics, MDPI, vol. 12(15), pages 1-21, July.

    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:12:y:2024:i:16:p:2447-:d:1451452. 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.