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2021 Vol.31, Issue 3 Preview Page

Research Article

Variation of Seasonal Groundwater Recharge Analyzed Using Landsat-8 OLI Data and a CART Algorithm

CART알고리즘과 Landsat-8 위성영상 분석을 통한 계절별 지하수함양량 변화

Seunghyuk Park1
,
Gyo-Cheol Jeong2*
박 승혁1
,
정 교철2*
1General Manager, JoongangConsultant Co., Ltd.
2Professor, Department of Earth and Environmental Sciences, Andong National University
1중앙컨설턴트(주) 지하수환경부 부장
2안동대학교 지구환경과학과 교수
*Corresponding Author
30 September 2021. pp. 395-432
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Abstract

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MODFLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

Keywords
classification and regression tree (CART)
groundwater recharge
Landsat-8
modified perpendicular vegetation index (mPVI)
normalized difference vegetation index (NDVI)
normalized difference tillage index (NDTI)
normalized difference residue index (NDRI)

지하수함양은 시공간적으로 다양하여 직접적으로 측정하기 어렵기 때문에 함양추정을 위해 수치모델이 널리 사용되고 있다. 이 연구에서는 지하수함양을 추정하기 위한 방법으로 기계학습법의 하나인 분류회귀트리(CART)모형을 적용하기 위해 수정된 수직식생지수(mPVI), 정규식생지수(NDVI), 정규경작지수(NDTI), 정규나지지수(NDRI) 같은 토양-식생관련 지수와 강우, 지형인자(고도, 경사, 경사방향)를 입력하고 김천지역 SWAT-MODFLOW의 함양량 결과를 추출 및 학습하여 함양량을 예측하였다. SWAT-MODFLOW의 함양량 분포에 대한 CART모형의 예측값의 전반적인 정확도는 0.5~0.7, 카파계수는 0.3~0.6으로 나타나 위성영상자료를 통해 토양-식생에 따른 함양량 변화를 합리적으로 예측할 수 있었다.

키워드
분류회귀트리(CART)모형
지하수함양량
Landsat-8
수정 수직식생지수(mPVI)
정규식생지수(NDVI)
정규경작지수(NDTI)
정규나지지수(NDRI)
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Information
  • Publisher :Korean Society of Engineering Geology
  • Publisher(Ko) :대한지질공학회
  • Journal Title :The Journal of Engineering Geology
  • Journal Title(Ko) :지질공학
  • Volume : 31
  • No :3
  • Pages :395-432
  • Received Date : 2021-08-30
  • Accepted Date : 2021-09-14
  • DOI :https://doi.org/10.9720/kseg.2021.3.395
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