Global warming due to the increase of greenhouse gases may significantly affect various aspects of the Earth’s environment and human life. In particular, the impacts of climate change on agriculture would be severe, leading to damages to crop yields. This paper examines the experimental prediction of rice yield in China using DNN (deep neural network) and climate model data for the period between 1979 and 2009. The DNN model built through the process of hyperparameter optimization can mitigate an overfitting problem and cope with outlier cases. Our model showed approximately 38.7% improved accuracy than the MLR (multiple linear regression) model, in terms of correlation coefficient with the yield statistics. We found that the diurnal temperature range and potential evapotranspiration were the critical factors for rice yield prediction. Our DNN model was also robust to extreme conditions such as drought in 2006 and 2007 in China, which showed its applicability to the future simulation of crop yields under climate change.

Abstract
1. 서론
2. 자료와 방법
    1) 연구지역
    2) 사용자료
    3) 분석방법
3. 결과 및 토의
4. 결론
References

• 조수빈(부경대학교 지구환경시스템과학부 공간정보시스템공학전공 석사과정) | Subin Cho (Master’s student, Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
• 김나리(부경대학교 지오메틱연구소 박사후연구원) | Nari Kim (Postdoctoral researcher, Geomatics Research Institute, Pukyong National University)
• 이수진(부경대학교 지구환경시스템과학부 공간정보시스템공학전공 박사과정) | Soo-Jin Lee (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
• 하경자(부산대학교 대기환경과학과) | Kyung-Ja Ha (Department of Atmospheric Sciences, Pusan National University)
• 이양원(부경대학교 공간정보시스템공학과) | Yang-Won Lee (Department of Spatial Information Engineering, Pukyong National University) Correspondence