Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning

Chen, Ling; Cai, Yaya; Ding, Yifang; Lv, Mingqi; Yuan, Cuili; Chen, Gencai

doi:10.1145/2971648.2971725

Cited by 33 publications

(18 citation statements)

References 28 publications

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“…The ML statistical model of a boosted regression tree (BRT) was used to reflect the relative influence of 2D and 3D indicators with land surface temperature in the other work (Alavipanah et al, 2018). Ling Chen et al, employed semi supervised learning (semi-EP) to establish the relationship between the various data sources and urban air quality in 2016 (Chen et al, 2016). In other case to understand the relationship between urban form and temperature moderation in Doha, three statistical approaches: Ordinary Least Squares (OLS), Regression Tree Analysis (RTA) and Random Forest (RF) were employed.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

Eslamirad

Kolbadinejad

Mahdavinejad

et al. 2020

SASBE

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PurposeThis research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method – by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones – to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results.Design/methodology/approachThe tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort – predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output.FindingsThe process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases – all over their life spans.Research limitations/implicationsLimitations of the study are related to the study variables and alternatives that are notable impact on the findings. Furthermore, the most trustable input data will result in the more accuracy in output. Then modeling and simulation processes are most significant part of the research to reach the exact results in the final step.Practical implicationsFinding of the study can be helpful in urban design strategies. By finding outdoor thermal comfort that resulted from machine learning method, urban and landscape designers, policymakers and architects are able to estimate the features of their designs in air quality and urban health and can be sure in catching design goals in case of thermal comfort in urban atmosphere.Social implicationsBy 2030, cities are delved as living spaces for about three out of five people. As green infrastructures influence in moderating the cities’ climate, the relationship between green spaces and habitants’ thermal comfort is deduced. Although the strategies to outside thermal comfort improvement, by design methods and applicants, are not new subject to discuss, applying machines that may be common in predicting results can be called as a new insight in applying more effective design strategies and in urban environment’s comfort preparation. Then study’s footprint in social implications stems in learning from the previous projects and developing more efficient strategies to prepare cities as the more comfortable and healthy places to live, with the more efficient models and consuming money and time.Originality/valueThe study achievements are expected to be applied not only in Tehran but also in other climate zones as the pattern in more eco-city design strategies. Although some similar studies are done in different majors, the concept of study is new vision in urban studies.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

Eslamirad

Kolbadinejad

Mahdavinejad

et al. 2020

SASBE

View full text Add to dashboard Cite

show abstract

“…Zheng et al [Zheng et al, 2013] put forward a semi-supervised learning approach based on a co-training framework. Chen et al [Chen et al, 2016] improve [Zheng et al, 2013] by selecting k nearest neighboring stations, instead of randomly selected stations, to model spatial correlations. ADAIN [Cheng et al, 2018] represents the state of the art of fine-grained air quality prediction.…”

Section: Related Workmentioning

confidence: 99%

“…To reduce the harmful effects of air pollution, accurately predicting air quality is of great importance for both governments and the public. The capability of providing fine-grained air quality inference is especially critical to, for example, guide people to make proper plans to avoid adverse effects on health through the air they breathe [Zheng et al, 2013;Chen et al, 2016;Cheng et al, 2018]. Yet, precisely predicting fine-grained air quality is technically challenging.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Air Quality Inference via Multi-Channel Attention Model

Han

Lü

Chen

2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.

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“…Zheng et al [33] proposed a semi-supervised machine learning method to estimate air pollution in grids not covered by monitoring stations by jointly training a spatial classifier (ANN) utilizing spatial features including POIs and road networks, and a temporal classifier (conditional random field (CRF)) using temporal features including meteorology, traffic, and human mobility. Along this line, Chen et al [34] proposed a semi-supervised ensemble learning model for air pollution estimation at a target grid, highlighting the importance of selecting spatial features from the nearest grids having monitoring data and sharing similar characteristics. Further, Zhu et al [35] proposed a Granger-causalitybased data-driven model to estimate air pollution levels in a target grid, based on Granger-causal urban dynamics obtained from the most influential grids (which could be geographically far away).…”

Section: Traditional Data-driven Urban Air Quality Modelingmentioning

confidence: 99%

Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities

Han¹,

Zhang²,

Li³

et al. 2021

Preprint

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Air pollution has long been a serious environmental health challenge, especially in metropolitan cities, where air pollutant concentrations are exacerbated by the street canyon effect and high building density. Whilst accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models fail to fully address the complex interaction between air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep learning framework that combines a convolutional neural network with a long short-term memory network, aims to address this gap to provide fine-grained city-wide air pollution estimation and station-wide forecast. Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect. Using Hong Kong and Beijing as case studies, Deep-AIR achieves a higher accuracy than our baseline models. Our model attains an accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr, and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%, 75.3%, and 63.5% for Beijing. Our saliency analysis has revealed that for Hong Kong, street canyon and road density are the best estimators for NO 2 , while meteorology is the best estimator for PM 2.5 .

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Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning

Cited by 33 publications

References 28 publications

Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

Fine-Grained Air Quality Inference via Multi-Channel Attention Model

Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities

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