기계학습 모형을 이용한 목조문화재 흰개미 피해 예측 방법
A Method for Predicting Termite Damage in Wooden Cultural Properties Using a Machine Learning Model
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초록
본 연구는 목조문화재 흰개미 피해와 기상데이터를 이용하여 기계학습 모형을 수립하고 예측 성능을 평가하였다. 데이터는 흰개미 피해 데이터, 목조문화재 위치, 기상데이터로 구분하였으며, 목조문화재 위치를 기준으로 가까운 3개의 관측지점을 탐색하고, 2010년부터 2018년까지 약 8년의 기상데이터를 종합하여 총 491개의 데이터 셋을 만들었다. 정제된 데이터 셋을 이용하여 기계학습 모형을 만들어 평가한 결과, 흰개미 피해에 직접적인 영향을 미치는 기상요인으로 소형증발량이 모형을 가장 잘 설명해주는 시계열 독립변수라는 것을 확인하였으며, Linear SVM 알고리즘 모형을 이용하였을 때 72.8%의 정확도를 나타내었다. 소형증발량은기상청 종관기상 데이터로 모든 관측소에서 관측되는 기상요인이 아니라 특정 관측소에서만수집되고 있어 예측 모형을 만들기에 충분한 데이터 확보가 어렵다. 기계학습 모형은 데이터수가 충분할 때 정확도를 높일 수 있기 때문에 흰개미 피해 데이터와 소형증발량 데이터가 더확보된다면 예측 성능을 높일 수 있을 것이다.
Abstract
In this study, a machine learning model was established using termite damage andmeteorological data on wooden cultural properties, and the prediction performance was evaluated. Thedata were divided into termite damage data, the location of wooden cultural properties, andmeteorological data. Three observatories were searched based on the location of the wooden culturalproperties, and meteorological data for about 8 years from 2010 to 2018 were combined to makea total of 491 data sets. As a result, it was confirmed that the value of small evaporation as ameteorological factor that directly affects termite damage is the time series independent variable thatbest explains the model, and showed an accuracy of 72.8% when the Linear SVM algorithm modelwas used. The value of small evaporation is the synoptic meteorological data of the KoreaMeteorological Administration, and it is collected only at specific stations, not meteorological factorsobserved at all stations. Therefore, It is difficult to obtain enough data to make a predictive model. Since machine learning models can improve accuracy when the number of data is sufficient, predictionperformance can be improved if more termite damage data and the value of small evaporation areobtained.
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