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@article{ART002731520},
author={LEE WOOSEOP and Kim Hyungkyoo},
title={Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City},
journal={The Korea Spatial Planning Review},
issn={1229-8638},
year={2021},
volume={109},
pages={89-104},
doi={10.15793/kspr.2021.109..006}

TY - JOUR
AU - LEE WOOSEOP
AU - Kim Hyungkyoo
TI - Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City
JO - The Korea Spatial Planning Review
PY - 2021
VL - 109
IS - null
PB - 국토연구원
SP - 89
EP - 104
SN - 1229-8638
AB - As the annual average temperature continues to rise due to climate change caused by global warming, the incidence of heat diseases and the number of deaths are also increasing, which is expected to require various alternatives and research. In this study, the average temperature rise-related variables are extracted through statistical analysis for Wonju City, where the average temperature increase rate and change are high, and the average temperature is predicted by utilizing deep learning-based LSTM and GRU based on the extracted variables. Three models were extracted through correlation and regression analysis for 26 variables collected based on prior research consideration, based on which LSTM and GRU analysis were conducted. The analysis showed the lowest MSE of LSTM – 0.4399(2.94°C), GRU – 0.4444(2.97°C) in the third model with 12 variables, with little MAE difference between validation and test data. This study is significant in that it extracted variables through statistical analysis and predicted average temperature rise using deep learning as a data acquisition method for adapting the annual average temperature rise problem. In addition, it is expected that urban space factors that affect the average temperature rise in Wonju City will be extracted along with predicting the trend of average temperature change, and appropriate measures will be prepared to take into account regional impact factors, not uniform climate change adaptation.
KW - Prediction of Average temperature;Deep-Learning;LSTM;GRU;Wonju
DO - 10.15793/kspr.2021.109..006
ER -

LEE WOOSEOP and Kim Hyungkyoo. (2021). Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City. The Korea Spatial Planning Review, 109, 89-104.

LEE WOOSEOP and Kim Hyungkyoo. 2021, "Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City", The Korea Spatial Planning Review, vol.109, pp.89-104. Available from: doi:10.15793/kspr.2021.109..006

LEE WOOSEOP, Kim Hyungkyoo "Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City" The Korea Spatial Planning Review 109 pp.89-104 (2021) : 89.

LEE WOOSEOP, Kim Hyungkyoo. Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City. 2021; 109 89-104. Available from: doi:10.15793/kspr.2021.109..006

LEE WOOSEOP and Kim Hyungkyoo. "Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City" The Korea Spatial Planning Review 109(2021) : 89-104.doi: 10.15793/kspr.2021.109..006

LEE WOOSEOP; Kim Hyungkyoo. Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City. The Korea Spatial Planning Review, 109, 89-104. doi: 10.15793/kspr.2021.109..006

LEE WOOSEOP; Kim Hyungkyoo. Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City. The Korea Spatial Planning Review. 2021; 109 89-104. doi: 10.15793/kspr.2021.109..006

LEE WOOSEOP, Kim Hyungkyoo. Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City. 2021; 109 89-104. Available from: doi:10.15793/kspr.2021.109..006

LEE WOOSEOP and Kim Hyungkyoo. "Prediction Model of Average Temperature based on Characteristic of Urban-space Using LSTM and GRU: The Case of Wonju City" The Korea Spatial Planning Review 109(2021) : 89-104.doi: 10.15793/kspr.2021.109..006