A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.</p

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

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data

Air pollution prediction with multi-modal data and deep neural networks

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem

The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air pollutant concentrations based on severity over various time periods. The modeling was performed in two parts: the first stage focused on object detection using satellite data of farm images to identify and count the number of cattle; the second stage predicted the next hour air pollutant concentration of the seven cattle farm air pollutants considered. The output from the second stage was then visualized based on severity, and analytics were performed on the historical data. The visualization illustrates the relationship between cattle count and air pollutants, an important factor for analyzing the pollutant concentration trend. We proposed the models Detectron2, YOLOv4, RetinaNet, and YOLOv5 for the first stage, and LSTM (single/multi lag), CNN-LSTM, and Bi-LSTM for the second stage. YOLOv5 performed best in stage one with an average precision of 0.916 and recall of 0.912, with the average precision and recall for all models being above 0.87. For stage two, CNN-LSTM performed well with an MAE of 3.511 and an MAPE of 0.016, while a stacked model had an MAE of 5.010 and an MAPE of 0.023

APPLICATION OF MACHINE LEARNING TO FILL IN THE MISSING MONITORING DATA OF AIR QUALITY

In this paper, three machine learning models have been applied to predict and fill in the missing monitoring data of air quality for Gia Lam and Nha Trang stations in Hanoi and Khanh Hoa respectively, including Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Two air pollutants being NO2 and PM10 were selected for this study. The experimental results showed that the performance of all three studied models is better than that of some traditional approaches, including Multiple Linear Regression (LR) and Spline interpolation. Besides that, ARMA, ANN and SVR can capture the fluctuation of concentrations of the selected pollutants. These results indicated that the machine learning is a feasible approach to deal with the missing of data which is one of the biggest problems of air quality monitoring stations in Viet Nam.