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

Annisa Nur Falah, Yudhie Andriyana (), Budi Nurani Ruchjana, Eddy Hermawan, Teguh Harjana, Edy Maryadi, Risyanto, Haries Satyawardhana and Sinta Berliana Sipayung
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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

Mathematics, 2024, vol. 12, issue 16, 1-21

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.

Keywords: expanded spatial Durbin model; ordinary kriging; data analytics life cycle; climate data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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