2023
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
A set of deep learning algorithms for air quality prediction applications Journal Article
In: Software Impacts, vol. 17, pp. 100562, 2023, ISSN: 2665-9638.
Abstract | Links | BibTeX | Tags: geospatial analysis, machine learning, spatiotemporal prediction
@article{Iskandaryan2023d,
title = {A set of deep learning algorithms for air quality prediction applications},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1016/j.simpa.2023.100562},
issn = {2665-9638},
year = {2023},
date = {2023-08-10},
journal = {Software Impacts},
volume = {17},
pages = {100562},
abstract = {This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air quality. The methods were implemented on a spatiotemporal combination of air quality, meteorological and traffic data of the city of Madrid. The two methods are exposed to be reused for prediction in other scenarios and different air quality phenomena.},
keywords = {geospatial analysis, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air quality. The methods were implemented on a spatiotemporal combination of air quality, meteorological and traffic data of the city of Madrid. The two methods are exposed to be reused for prediction in other scenarios and different air quality phenomena.
Iskandaryan, Ditsuhi
Universitat Jaume I. INIT, 2023.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning, spatiotemporal prediction
@phdthesis{Iskandaryan2023c,
title = {Study and Prediction of Air Quality in Smart Cities through Machine Learning Techniques Considering Spatiotemporal Components},
author = {Ditsuhi Iskandaryan},
doi = {http://dx.doi.org/10.6035/14101.2023.726676},
year = {2023},
date = {2023-03-07},
school = {Universitat Jaume I. INIT},
abstract = {Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.},
keywords = {air quality prediction, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {phdthesis}
}
Air quality is considered one of the top concerns. Information and knowledge about air quality can assist in effectively monitoring and controlling concentrations, reducing or preventing its harmful impacts and consequences. The complexity of air quality dependence on various components in spatiotemporal dimensions creates additional challenges to acquire this information. The current dissertation proposes machine learning and deep learning technologies that are capable of capturing and processing multidimensional information and complex dependencies controlling air quality. The following components come together to formulate the novelty of the current work: spatiotemporal forecast of the defined prediction target (nitrogen dioxide); incorporation and integration of air quality, meteorological and traffic data with their features/variables in spatiotemporal dimensions within a certain spatial extent and temporal interval; the consideration of coronavirus disease 2019 as an external key factor impacting air quality level; and provision of the code and data implemented to incentivise and guarantee reproducibility.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components Journal Article
In: Data in Brief, vol. 47, no. 108957, 2023, ISSN: 352-3409.
Abstract | Links | BibTeX | Tags: geospatial analysis, geospatial data, nitrogen dioxide prediction, spatiotemporal prediction
@article{Iskandaryan2023b,
title = {Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1016/j.dib.2023.108957},
issn = {352-3409},
year = {2023},
date = {2023-02-06},
journal = {Data in Brief},
volume = {47},
number = {108957},
abstract = {This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms. The raw dataset is obtained from the Open Data portal of the Madrid City Council.},
keywords = {geospatial analysis, geospatial data, nitrogen dioxide prediction, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms. The raw dataset is obtained from the Open Data portal of the Madrid City Council.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Graph Neural Network for Air Quality Prediction: A Case Study in Madrid Journal Article
In: IEEE Access, vol. 11, pp. 2729-2742, 2023, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning, spatiotemporal prediction
@article{Iskandaryan2023a,
title = {Graph Neural Network for Air Quality Prediction: A Case Study in Madrid},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {10.1109/ACCESS.2023.3234214},
issn = {2169-3536},
year = {2023},
date = {2023-01-04},
journal = {IEEE Access},
volume = {11},
pages = {2729-2742},
abstract = {Air quality monitoring, modelling and forecasting are considered pressing and challenging topics for citizens and decision-makers, including the government. The tools used to achieve the above goals vary depending on the opportunities provided by technological development. Much attention is currently being paid to machine learning and deep learning methods, which, compared to domain knowledge methods, often perform better in terms of capturing, computing and processing multidimensional information and complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network. In the framework of the current study, it is initially suggested to use the presented approach in the domain of air quality prediction. The proposed method was tested using air quality, meteorological and traffic data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit).},
keywords = {air quality prediction, machine learning, spatiotemporal prediction},
pubstate = {published},
tppubtype = {article}
}
Air quality monitoring, modelling and forecasting are considered pressing and challenging topics for citizens and decision-makers, including the government. The tools used to achieve the above goals vary depending on the opportunities provided by technological development. Much attention is currently being paid to machine learning and deep learning methods, which, compared to domain knowledge methods, often perform better in terms of capturing, computing and processing multidimensional information and complex dependencies. The technique introduced in this work is an Attention Temporal Graph Convolutional Network based on a combination of Attention, a Gated Recurrent Unit and a Graph Convolutional Network. In the framework of the current study, it is initially suggested to use the presented approach in the domain of air quality prediction. The proposed method was tested using air quality, meteorological and traffic data obtained from the city of Madrid for the periods January-June 2019 and January-June 2022. The evaluation metrics, including Root Mean Square Error, Mean Absolute Error and Pearson Correlation Coefficient, confirmed the proposed model’s advantages compared with the reference models (Temporal Graph Convolutional Network, Long Short-Term Memory and Gated Recurrent Unit).