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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
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    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.
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    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 1-20, July.

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