한국초지조사료학회지 (JKSGFS) Vol. 42 No. 2 (p.127-136)

이상기상 시 사일리지용 옥수수의 기계학습을 이용한 피해량 산출

Damage of Whole Crop Maize in Abnormal Climate Using Machine Learning
키워드 :
Abnormal climate,Damage,Machine learning,Whole crop maize,Yield prediction model

목차

ABSTRACT
Ⅰ. 서론
Ⅱ. 재료 및 방법
   1. 데이터 수집
   2. 데이터 가공
   3. 수량예측모델 제작
   4. 이상기상 피해량 산정
   5. 이상기상 피해량 전자지도 제시
Ⅲ. 결과 및 고찰
   1. 이상기상 수준에 따른 WCM의 DMY 예측값
   2. 이상기상 수준에 따른 WCM의 피해량
   3. 이상기상 피해량 전자지도 제시
Ⅳ. 요약
Ⅴ. 사사
Ⅵ. REFERENCES

초록

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMYnormal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 ㎧). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).