Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Spatiotemporal Deep Learning 모델을 기반으로 한 도시 전역의 대기 오염 보간과 예측

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 차상균.대기 오염은 대도시에서 가장 큰 문제 중 하나이다. 많은 국가들은 주요 도시 주변에 대기 오염 모니터링 센터를 건설하여 대기 오염 물질을 수집하고 해당 지역의 시민들에게 대기 오염을 경고한다. 그러나 도시에서의 대기 오염은 균일하지 않으며 시공간 (spatiotemporal)적인 문제이다. 대기 오염은 위치 (공간적 특성)과 시각 (시간적 특성)에 따라 달라진다. 따라서, 도시 전체의 대기 오염 보간과 예측은 시민들이 시간과 공간에 대해 대기의 질을 파악하고, 나아가 건강에 대한 위협을 제거하기 위한 필요 조건이다. 대기 오염은 도시 전역의 여러 시공간적 요인에 의해 영향을 받는 것으로 알려져 있다. 그 중, 기상이 대기 오염에 가장 큰 영향을 주는 것으로 인식되고 있다. 그 외에, 교통량은 대기 오염의 주요 원인인 도로의 차량 밀도를 반영한다. 평균 주행 속도는 도시 대기 오염에 영향을 준다고 판단되는 교통 체증을 나타낸다. 마지막으로, 외부 대기 오염원은 도시 대기 오염 문제의 근원 중 하나라고 주장된다. 본 논문에서는 서울시의 대기 오염 데이터, 기상 데이터, 교통량, 평균 주행 속도와 같은 많은 시공간적 데이터와 서울의 대기 오염에 영향을 준다고 알려진 중국의 3개 지방(베이징, 상하이, 산동)의 대기 오염 데이터를 제시하였다. 대기 오염에 대한 최근의 연구에서는 특정 위치와 시간의 대기 오염 예측 모델을 구축하려고 시도해왔다. 그러나 대부분 연속되지 않은 위치에대한 대기 오염을 예측하거나 직접 만든 공간 및 시간적 특성을 사용하는 데 중점을 두었다. 최근 CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) 및 LSTM (Long-Short Term Memory)과 같은 딥러닝 모델이 공간 및 시간 관련 문제에서 우수하다고 알려져있다. 본 논문에서는 CNN과 LSTM을 결합한 ConvLSTM (Convolutional Long-Short Term Memory) 모델을 제안하였으며, 이를 통해 데이터의 공간 및 시간적 특성을 효율적으로 처리하고 최근의 다른 연구 결과보다 뛰어난 성능을 달성하였다.Air pollution is one of the most concerns of big cities. Many countries in the world have constructed air quality monitoring stations around major cities to collect air pollutants and make the warning to urban citizens about the air pollution around them. However, air pollution is not uniform in the city, but it is a spatiotemporal problem. It changes by locations (spatial feature) and by time (temporal feature). Consequently, citywide air pollution interpolation and prediction is a requirement of urban people to know the air quality through time and spaces to eliminate the health risks. Moreover, air pollution is affected by many spatiotemporal factors throughout the whole city. Among them, meteorology is recognized to be one the most significant effects to air pollution. Besides that, traffic volume reflects the density of vehicles on roads which is the primary cause of air pollution. Average driving speed indicates the traffic congestion which also reasonably influences air pollution over the city. Finally, external air pollution sources from outside areas are claimed to be the reason contributing to a city's air pollution problem. In this thesis, we present many spatiotemporal datasets collected over Seoul city, Korea such as air pollution data, meteorological data, traffic volume, average driving speed, and air pollution of 3 China areas like Beijing, Shanghai, Shandong, which are known to have the effect to Seoul's air pollution. Recent research in air pollution has tried to build models to predict air pollution by locations and in the future time. Nonetheless, they mostly focused on predicting air pollution in discrete locations or used hand-crafted spatial and temporal features. Recently, Deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) are known to be superior in spatial and temporal relating problems. In this thesis, we propose the usage of Convolutional Long-Short Term Memory (ConvLSTM) model, a combination of CNN and LSTM, which efficiently manipulates the spatial and temporal features of the data and outperforms other recent research.1 INTRODUCTION 1 1.1 Air pollution description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Citywide Air pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Spatiotemporal datasets introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 2 RELATED WORK 11 2.1 Spatiotemporal Air pollution interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Machine Learning/Neural Networks based Air pollution prediction models . . . .12 2.3 Spatiotemporal Deep Learning models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Spatiotemporal Deep Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.1 CNN and LSTM models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16 3.2 ConvLSTM model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Air Pollution Interpolation and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 EXPERIMENTS AND EVALUATIONS 29 4.1 Baselines description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Experiments and Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1 Air pollution Interpolation: experiments and evaluations . . . . . . . . . . . . . . . . . 34 4.2.2 Air pollution Forecasting: experiments and evaluations . . . . . . . . . . . . . . . . . . 41 5 CONCLUSIONS AND FUTURE WORK 45 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Maste

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization