PlumX Metrics
Embed PlumX Metrics

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

Mathematics, ISSN: 2227-7390, Vol: 12, Issue: 16
2024
  • 1
    Citations
  • 0
    Usage
  • 6
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
    • Citation Indexes
      1
  • Captures
    6
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Recent Findings from Universitas Padjadjaran Highlight Research in Information and Data Analytics (An Expanded Spatial Durbin Model with Ordinary Kriging of Unobserved Big Climate Data)

2024 AUG 27 (NewsRx) -- By a News Reporter-Staff News Editor at Politics, Law & Government Daily -- New research on information and data analytics

Article Description

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.

Bibliographic Details

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

MDPI AG

Computer Science; Mathematics; Engineering

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know