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; 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.