Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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A study of artificial neural network for in-situ air temperature mapping using satellite data in urban area

위성 정보를 활용한 도심 지역 기온자료 지도화를 위한 인공신경망 적용 연구

  • Jeon, Hyunho (Department of Global Smart City, Sungkyunkwan University) ;
  • Jeong, Jaehwan (Center for Built Environment, Sungkyunkwan University) ;
  • Cho, Seongkeun (Department of Water Resources, Sungkyunkwan University) ;
  • Choi, Minha (Department of Water Resources, Sungkyunkwan University)
  • 전현호 (성균관대학교 글로벌스마트시티융합전공) ;
  • 정재환 (성균관대학교 건설환경연구소) ;
  • 조성근 (성균관대학교 수자원학과) ;
  • 최민하 (성균관대학교 수자원학과)
  • Received : 2022.08.23
  • Accepted : 2022.10.05
  • Published : 2022.11.30
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Abstract

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

본 연구에서는 서울시 기온 지상관측 자료의 지도화를 위해 Artificial Neural Network (ANN)을 사용하였다. 지도화를 위한 보조자료로는 MODerate resolution Imaging Spectroradiometer (MODIS) 자료를 사용하였다. ANN 모델 설계를 위해 입력자료와 출력자료 간의 산점도 및 통계분석을 수행하였으며, 기온과의 상관성이 비교적 높게 나타나는 입력자료인 지표면온도, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI)와 시간(위성관측시각, Day of year), 위치(위도, 경도), 데이터 품질(운량)과 관련된 데이터 종류를 분류 및 조합하여 학습을 진행하였다. 기온자료와 상관성이 높은 데이터만으로 학습을 진행하였을 때 상관계수(r)와 Root Mean Squared Error (RMSE)의 평균값이 0.9667, 2.708℃로 우수한 성능을 보였다. 학습에 사용된 데이터의 종류가 추가될수록 더 우수한 학습 결과를 보였으며, 모든 데이터가 활용될 때에는 r과 RMSE의 평균값이 0.9840, 1.883℃로 가장 우수한 성능을 보였다. ANN 모델으로 생성한 서울시 기온 지도에서는 픽셀별 지형적 특성에 적절하게 기온이 산정된 것으로 판단되며, 추후 연구지역 확대 및 위성자료의 다양화를 통해 시단위 및 전국단위 기온 분포 분석 연구가 가능할 것이다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 RS-2022-00155763). 본 연구는 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(NRF-2022R1A2C2010266)과 교육부 및 한국연구재단의 4단계 두뇌한국21 사업(4단계 BK21 사업)과 2021년도 정부(교육부)의 재원으로 한국연구재단 (NRF-2021R1A6A3A01087645)의 지원을 받아 수행된 연구임.

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