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Predicting Provincial Gross Domestic Product Using Satellite Data and Machine Learning Methods: A Case Study of Thailand

Asian development review, 2023-09, Vol.40 (2), p.39-85 [Peer Reviewed Journal]

2023. This work is published under https://creativecommons.org/licenses/by/3.0/ (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0116-1105 ;EISSN: 1996-7241 ;DOI: 10.1142/S0116110523400024

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  • Title:
    Predicting Provincial Gross Domestic Product Using Satellite Data and Machine Learning Methods: A Case Study of Thailand
  • Author: PUTTANAPONG, NATTAPONG ; PRASERTSOONG, NUTCHAPON ; PEECHAPAT, WICHAYA
  • Subjects: Case studies ; COVID-19 ; Data ; Economic growth ; Environmental aspects ; GDP ; Google Earth Engine ; Gross Domestic Product ; Machine learning ; Measurement ; Open source software ; Regional development ; satellite data ; Satellites ; Spatial data ; Thailand
  • Is Part Of: Asian development review, 2023-09, Vol.40 (2), p.39-85
  • Description: This study introduced a new approach for monitoring regional development by applying satellite data with machine learning algorithms. Satellite data that represent physical features and environmental factors were obtained by developing a web-based application on the Google Earth Engine platform. Four machine learning methods were applied to the obtained geospatial data to predict provincial gross domestic product. The random forest method achieved the highest predictive performance, with 97.7% accuracy. The constructed random forest model was extended to conduct variable importance and minimal depth analyses, enabling the quantification of a factor’s influence on the prediction outcome. Variable importance and minimal depth analyses generated similar results, indicating that urban area and population are the most influential factors. Moreover, environmental and climate indicators exert medium-level effects. This study showed that integrating available satellite data and machine learning methods could be an alternative framework for facilitating a timely and costless monitoring system of regional development.
  • Publisher: Manila: Asian Development Bank
  • Language: English
  • Identifier: ISSN: 0116-1105
    EISSN: 1996-7241
    DOI: 10.1142/S0116110523400024
  • Source: Free E Journals
    Digital Library of the Commons
    Coronavirus Research Database
    ProQuest Central
    DOAJ Directory of Open Access Journals
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