Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools

Hassan, Rafiul; Li, Min

doi:10.5772/10049

Cited by 7 publications

(4 citation statements)

References 34 publications

(37 reference statements)

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“…The choice of this machine learning algorithm is strongly based on its fast-computational attributes and its ability to learn and adapt to new instances. Hassan et al [25] noted that air quality prediction has complex and non-linear patterns. These patterns of data can be efficiently handled by neural networks.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Data-Driven Air Quality Characterization for Urban Environments: A Case Study

Zhou

Ewa

et al. 2018

IEEE Access

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The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

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Section: Deep Learning Approachesmentioning

confidence: 99%

Data-Driven Air Quality Characterization for Urban Environments: A Case Study

Zhou

Ewa

et al. 2018

IEEE Access

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“…Hassan and Li's approach relies, as many other works, on AI to predict specific pollutant concentrations in restricted areas [46]. These works include forecasting methods based on artificial neural networks (ANN) [47,48,49], support vector machine (SVM) [50,51], fuzzy logic [52], and a combination of fuzzy logic and Hidden Markov Model (HMM) [53].…”

Section: Related Workmentioning

confidence: 99%

Using geosocial search for urban air pollution monitoring

Sammarco

Tse

Pau

et al. 2017

Pervasive and Mobile Computing

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“…Recently, many deep learning forecasting methods based on long short-term memory (LSTM) networks are applied to forecast air pollutant concentrations and their spatial distribution [17][18][19][20]. These models have the advantage of learning long-term dependencies on air pollution data.…”

Section: Introductionmentioning

confidence: 99%

A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios

Cardone,

Di Martino,

Miraglia

2024

Computers

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This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population.

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Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools

Cited by 7 publications

References 34 publications

Data-Driven Air Quality Characterization for Urban Environments: A Case Study

Data-Driven Air Quality Characterization for Urban Environments: A Case Study

Using geosocial search for urban air pollution monitoring

A GIS-Based Fuzzy Model to Detect Critical Polluted Urban Areas in Presence of Heatwave Scenarios

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