Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

Sumin Park; Haemi Park; Jungho Im; Cheolhee Yoo; Jinyoung Rhee; Byungdoo Lee; ChunGeun Kwon

doi:10.1371/journal.pone.0223362

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

PLoS One. 2019 Oct 10;14(10):e0223362. doi: 10.1371/journal.pone.0223362. eCollection 2019.

Authors

Sumin Park¹, Haemi Park^{1

2}, Jungho Im¹, Cheolhee Yoo¹, Jinyoung Rhee³, Byungdoo Lee⁴, ChunGeun Kwon⁴

Affiliations

¹ School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
² Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
³ Climate Analytics Department, APEC Climate Center, Busan, South Korea.
⁴ Forest Conservation Department, National Institute of Forest Science, Seoul, South Korea.

Abstract

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Köppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC).

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Calibration
Climate*
Geography
Machine Learning*
Models, Theoretical
Neural Networks, Computer
Republic of Korea

Grants and funding

This research was supported by the National Research Foundation of Korea (NRF- 2017M1A3A3A02015981; NRF-2017R1D1A1B03028129; NRF-2018K2A9A2A06023758) and the Korea Meteorological Administration (KMIPA 2017-7010). This research was also partially supported by a grant from the National Institute of Forest Science, Korea. CY was also supported by Global PhD Fellowship Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2018H1A2A1062207). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.