AQNet: 깊은 생성 모델을 이용한 대기 질의 시공간적 예측

학위논문(석사)--서울대학교 대학원 :공과대학 전기·정보공학부,2019. 8. Cha, Sang Kyun.With the increase of global economic activities and high energy demand, many countries have concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this thesis, we propose a deep generative model for spatio-temporal air quality prediction, entitled AQNet. Unlike previous work, our model transforms air quality index data into 2D frames (heat-map images) for effectively capturing spatial relations of air quality levels among different areas. It then combines the spatial representation with temporal features of critical factors such as meteorology and external air pollution sources. For prediction, the model first generates heat-map images of future air quality levels, then aggregates them into output values of corresponding areas. Based on the analyses of data, we also assessed the impacts of critical factors on air quality prediction. To evaluate the proposed method, we conducted experiments on two real-world air pollution datasets: Seoul dataset and China 1-year dataset. For Seoul dataset, our method showed a 15.2%, 8.2% improvement in mean absolute error score for long-term predictions of PM2.5 and PM10, respectively compared to baselines and state-of-the-art methods. Also, our method improved mean absolute error score of PM2.5 predictions by 20% compared to the previous state-of-the-art results on China dataset.세계 경제 활동과 에너지 수요가 증가함에 따라 많은 국가들이 대기 오염에 대한 우려를 제기하고 있다. 하지만 많은 요인들의 복잡한 상호 작용으로 인해 대기 질을 예측하는 것은 어려운 문제다. 본 논문에서는 AQNet이라는 이름의 시공간적 대기 질 예측을 위한 심층 생성 모델을 제안한다. 이전 연구와 달리 이 모델은 대기 질 지수 데이터를 2D 프레임(히트 맵 이미지)으로 변환하여 대기 품질 수준의 영역간 공간적 관계를 효과적으로 포착한다. 그런 다음 기상과 외부 대기 오염원과 같은 중요한 요소의 시간적 특징과 공간 표현을 결합한다. 예측 모델은 먼저 미래의 대기 품질 수준의 히트 맵 이미지를 생성한 다음 해당 영역의 출력 값으로 집계한다. 데이터 분석을 토대로 대기 오염 예측에 각 주요 요소들이 미치는 영향을 평가하였다. 제안된 방법을 평가하기 위해 실제 대기 오염 데이터 세트인 서울의 데이터 세트와 중국의 1년 데이터 세트를 실험했다. 본 논문에서 제안한 방법은 서울 데이터세트에서 수행된 PM2.5와 PM10의 장기 예측에 대해 이전의 SOTA 방법과 비교하여 MAE 점수가 각각 15.2%, 8.2% 향상되었다. 또한 중국 데이터 세트에 대한 이전 연구와 비교하여 PM2.5 예측의 MAE 점수를 20% 향상시켰다.Abstract i Contents ii List of Tables iv List of Figures v 1 INTRODUCTION 1 1.1 Air Pollution Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of the Proposed Method . . . . . . . . . . . . . . . . . . . 2 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 RELATED WORK 5 2.1 Spatio-Temporal Prediction . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Air Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 OVERVIEW 8 3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4 DATA MANAGEMENT 11 4.1 Real-time Data Collecting . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Spatial Transformation Function . . . . . . . . . . . . . . . . . . . . 13 4.3.1 District-based Interpolation . . . . . . . . . . . . . . . . . . 14 4.3.2 Geo-based Interpolation . . . . . . . . . . . . . . . . . . . . 15 5 Proposed Method 17 5.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3.1 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3.2 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.3.3 Training Algorithm . . . . . . . . . . . . . . . . . . . . . . . 26 6 EXPERIMENTS 28 6.1 Baselines and State-of-the-art methods . . . . . . . . . . . . . . . . . 28 6.2 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.2.1 Implementation details . . . . . . . . . . . . . . . . . . . . . 29 6.2.2 Evaluation Metric . . . . . . . . . . . . . . . . . . . . . . . . 30 6.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.3.1 Performance on Spatial Module Selection . . . . . . . . . . . 31 6.3.2 Comparison to Baselines and State-of-the-art Methods . . . . 33 6.3.3 Evaluation on China 1-year Dataset . . . . . . . . . . . . . . 36 6.3.4 Assessing the Impact of Critical Factors . . . . . . . . . . . . 37 7 CONCLUSION 41 Abstract (In Korean) 47 Acknowlegement 48Maste

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