Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels

Attitudes expressed in online comments about environmental factors in the tourism sector: an exploratory study

The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified

An informatics based approach to respiratory healthcare.

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM

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

The impact of location on housing prices: applying the Artificial Neural Network Model as an analytical tool.

The location of a residential property in a city directly affects its market price. Each location represents different values in variables such as accessibility, neighbourhood, traffic, socio-economic level and proximity to green areas, among others. In addition, that location has an influence on the choice and on the offer price of each residential property. The development of artificial intelligence, allows us to use alternative tools to the traditional methods of econometric modelling. This has led us to conduct a study of the residential property market in the city of Valencia (Spain). In this study, we will attempt to explain the aspects that determine the demand for housing and the behaviour of prices in the urban space. We used an artificial neutral network as a price forecasting tool, since this system shows a considerable improvement in the accuracy of ratings over traditional models. With the help of this system, we attempted to quantify the impact on residential property prices of issues such as accessibility, level of service standards of public utilities, quality of urban planning, environmental surroundings and other locational aspects.