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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

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Title
Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique
Author
Cho, DongjinYoo, CheolheeIm, JunghoLee, YeonsuLee, Jaese
Issue Date
2020-07
Publisher
TAYLOR & FRANCIS LTD
Citation
GISCIENCE & REMOTE SENSING, v.57, no.5, pp.633 - 649
Abstract
The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T-max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T-max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.
URI
https://scholarworks.unist.ac.kr/handle/201301/32358
URL
https://www.tandfonline.com/doi/full/10.1080/15481603.2020.1766768
DOI
10.1080/15481603.2020.1766768
ISSN
1548-1603
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