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

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Stakeholder engagement in sustainable housing refurbishment in the UK

The UK government is committed to effectively implement a viable sustainable agenda in the social housing sector. To this end housing associations and local authorities are being encouraged to improve the environmental performance of their new and existing homes. Whilst much attention has been focused on new housing (e.g. the Code for Sustainable Homes) little effort has been focussed on improving the 3.9 (approx) million homes maintained and managed by the public sector (in England), which, given the low rate of new build and demolition (<1% in England), will represent approximately 70% of the public housing stock in 2050. Thus, if UK is to achieve sustainable public housing the major effort will have to focus on the existing stock. However, interpreting the sustainability agenda for an existing housing portfolio is not a straight foreword activity. In addition to finding a ‘technical’ solution, landlords also haveto address the socio-economic issues that balance quality of expectations of tenants with the economic realities of funding social housing refurbishment. This paper will report the findings of a qualitative study (participatory approach) that examined the processes by which a large public landlord sought to develop a long-term sustainable housing strategy. Through a series of individual meetings and group workshops the research team identified: committed leadership; attitudes towards technology; social awareness; and collective understanding of the sustainability agenda as key issues that the organisation needed to address in developing a robust and defendable refurbishment strategy. The paper concludes that the challenges faced by the landlord in improving the sustainability of their existing stock are not primarily technical, but socio-economic. Further, while the economic challenges: initial capital cost; lack of funding; and pay-back periods can be overcome, if the political will exists, by fiscal measures; the social challenges: health & wellbeing; poverty; security; space needs; behaviour change; education; and trust; are much more complex in nature and will require a coordinated approach from all the stakeholders involved in the wider community if they are to be effectively addressed. The key challenge to public housing landlords is to develop mechanisms that can identify and interpret the complex nature of the social sustainability agenda in a way that reflects local aspirations (although the authors believe the factors will exist in all social housing communities, their relative importance is likely to vary between communities) whilst addressing Government agendas

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