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Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Author

Listed:
  • Yudhie Andriyana

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

  • Annisa Nur Falah

    (Post Doctoral Program, 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)

  • Albertus Sulaiman

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

  • Daisy Lou Lim-Polestico

    (Center for Computational Analytics and Modeling, Premier Research Institute of Science and Mathematics, MSU-Iligan Institute of Technology, Iligan City 9200, Philippines)

Abstract

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2304-:d:1441121
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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.
    2. Robinson, Peter M. & Rossi, Francesca, 2015. "Refinements in maximum likelihood inference on spatial autocorrelation in panel data," Journal of Econometrics, Elsevier, vol. 189(2), pages 447-456.
    3. Jonathan Jalbert & Christian Genest & Luc Perreault, 2022. "Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 461-486, September.
    4. Smirnov, Oleg & Anselin, Luc, 2001. "Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach," Computational Statistics & Data Analysis, Elsevier, vol. 35(3), pages 301-319, January.
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