@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}
}

@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}
}

@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}
}