@inproceedings{Iskandaryan2022e,
title = {Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1007/978-3-031-18311-9_7},
isbn = {978-3-031-18311-9},
year = {2022},
date = {2022-11-10},
booktitle = {Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. },
pages = {111–128},
publisher = {Springer, Cham},
series = {Progress in IS},
abstract = {Air quality prediction, especially spatiotemporal prediction, is still a challenging issue. Considering the impact of numerous factors on air quality causes difficulties in integrating these factors in a spatiotemporal dimension and developing a model to make efficient predictions. At the same time, machine learning and deep learning development bring advanced approaches to addressing these challenges and propose novel solutions. The current work introduces one of the most advanced methods, an attention temporal graph convolutional network, which was implemented on datasets constructed by combining air quality, meteorological and traffic data on a spatiotemporal axis. The datasets were obtained from the city of Madrid for the periods January-June 2019 and January–June 2020. The evaluation metrics, the Root Mean Square Error and the Mean Absolute Error confirmed the proposed model’s advantages compared with long short-term memory (reference model). Particularly, it outperformed the latter method by 14.18% and 3.78%, respectively.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Iskandaryan2022c,
title = {Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide},
author = {Ditsuhi Iskandaryan and Silvana Di Sabatino and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = { https://doi.org/10.5194/agile-giss-3-6-2022},
year = {2022},
date = {2022-06-15},
booktitle = {AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science)},
volume = {3},
number = {6},
publisher = {Copernicus Publications},
abstract = {Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{SanchezPozo2021a,
title = {Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive},
author = {Nadia N Sánchez-Pozo and Sergio Trilles-Oliver and Albert Solé-Ribalta and Leandro L. Lorente-Leyva and Dagoberto Mayorca-Torres and Diego H Peluffo-Ordóñez},
doi = {https://doi.org/10.1007/978-3-030-86271-8_25},
isbn = {978-3-030-86271-8},
year = {2021},
date = {2021-09-15},
booktitle = {Hybrid Artificial Intelligent Systems (International Conference on Hybrid Artificial Intelligence Systems)},
pages = {293-304},
publisher = {Springer, Cham},
abstract = {This paper presents a comparative analysis of predictive models applied to air quality estimation. Currently, among other global issues, there is a high concern about air pollution, for this reason, there are several air quality indicators, with carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) being the main ones. When the concentration level of an indicator exceeds an established air quality safety threshold, it is considered harmful to human health, therefore, in cities like London, there are monitoring systems for air pollutants. This study aims to compare the efficiency of stochastic and heuristic predictive models for forecasting ozone (O3) concentration to estimate London's air quality by analyzing an open dataset retrieved from the London Datastore portal. Models based on data analysis have been widely used in air quality forecasting. This paper develops four predictive models (autoregressive integrated moving average - ARIMA, support vector regression - SVR, neural networks (specifically, long-short term memory - LSTM) and Facebook Prophet). Experimentally, ARIMA models and LSTM are proved to reach the highest accuracy in predicting the concentration of air pollutants among the considered models. As a result, the comparative analysis of the loss function (root-mean-square error) reveled that ARIMA and LSTM are the most suitable, accomplishing a low error rate of 0.18 and 0.20, respectively.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}