대기중 미세먼지 농도 증가와 미세먼지가 인체에 끼치는 해로움에 대한 관심이 커지면서 미세먼지에 대한 신체 노출을 줄이기 위한 방법으로 미세먼지 예측이 떠오르고 있다. 또한 지역별 미세먼지 수치 관측에 대한 요구가 증가하고 있지만 미세먼지 관측소가 부족해 요구를 만족하기 어렵다. 본 논문에서는 스마트 시티 데이터를 활용하여 미세먼지 관측소가 부족한 문제를 해결하고 미세먼지 수치를 예측하고자 한다. 스마트 시티 데이터 같은 시계열 데이터를 다루는 딥러닝 모델은 입력 데이터의 특성 수에 영향을 받기 때문에 미세먼지 예측에 적합한 모델을 찾아야한다. 데이터 특성이 다른 두 스마트 시티 데이터에 대해 다층 신경망, LSTM, CNN-LSTM 모델을 학습하여 RMSE, MAPE의 값과 표준편차를 비교하여 적합한 딥러닝 모델을 제시한다. 제시한 모델과 기존 연구들에서 사용된 모델을 비교한 결과 제시한 모델이 더 정확한 예측을 수행하였다.
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