2023
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
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).
2022
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks Proceedings Article
In: Advances and New Trends in Environmental Informatics. ENVIROINFO 2022. , pp. 111–128, Springer, Cham, 2022, ISBN: 978-3-031-18311-9.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@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}
}
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.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network Journal Article
In: International Journal of Computational Intelligence and Applications, vol. 21, no. 2, pp. 2250014, 2022, ISSN: 1757-5885.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@article{Iskandaryan2022d,
title = {Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1142/S1469026822500146},
issn = {1757-5885},
year = {2022},
date = {2022-06-21},
journal = {International Journal of Computational Intelligence and Applications},
volume = {21},
number = {2},
pages = {2250014},
abstract = {Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {article}
}
Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy.
Iskandaryan, Ditsuhi; Sabatino, Silvana Di; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide Proceedings Article
In: AGILE GIScience Series (Proceedings of the 25th AGILE Conference on Geographic Information Science), Copernicus Publications, 2022.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@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}
}
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.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid Journal Article
In: PLOS ONE, vol. 17, no. 6, pp. e0269295, 2022, ISSN: 932-6203.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@article{Iskandaryan2022b,
title = {Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.1371/journal.pone.0269295},
issn = {932-6203},
year = {2022},
date = {2022-05-01},
journal = {PLOS ONE},
volume = {17},
number = {6},
pages = {e0269295},
abstract = {Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management. One of the most advanced machine learning algorithms, Bidirectional convolutional LSTM, is being used in ongoing work to predict the concentration of nitrogen dioxide. The model has been validated to perform more accurate spatiotemporal analysis based on the integration of temporal and geospatial factors. The analysis was carried out according to two scenarios developed on the basis of selected features using data from the city of Madrid for the periods January-June 2019 and January-June 2020. Evaluation of the model’s performance was conducted using the Root Mean Square Error and the Mean Absolute Error which emphasises the superiority of the proposed model over the reference models. In addition, the significance of a feature selection technique providing improved accuracy was underlined. In terms of execution time, due to the complexity of the Bidirectional convolutional LSTM architecture, convergence and generalisation of the data took longer, resulting in the superiority of the reference models.},
keywords = {air quality prediction, machine learning},
pubstate = {published},
tppubtype = {article}
}
Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management. One of the most advanced machine learning algorithms, Bidirectional convolutional LSTM, is being used in ongoing work to predict the concentration of nitrogen dioxide. The model has been validated to perform more accurate spatiotemporal analysis based on the integration of temporal and geospatial factors. The analysis was carried out according to two scenarios developed on the basis of selected features using data from the city of Madrid for the periods January-June 2019 and January-June 2020. Evaluation of the model’s performance was conducted using the Root Mean Square Error and the Mean Absolute Error which emphasises the superiority of the proposed model over the reference models. In addition, the significance of a feature selection technique providing improved accuracy was underlined. In terms of execution time, due to the complexity of the Bidirectional convolutional LSTM architecture, convergence and generalisation of the data took longer, resulting in the superiority of the reference models.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Application of deep learning and machine learning in air quality modeling Book Chapter
In: Marques, Gonçalo; Ighalo, Joshua (Ed.): pp. 11-23, Elsevier, 2022, ISBN: 9780323855976.
Links | BibTeX | Tags: air quality prediction, deep learning, machine learning
@inbook{Iskandaryan2022a,
title = {Application of deep learning and machine learning in air quality modeling},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
editor = {Gonçalo Marques and Joshua Ighalo },
doi = {https://doi.org/10.1016/B978-0-323-85597-6.00018-5},
isbn = {9780323855976},
year = {2022},
date = {2022-03-30},
pages = {11-23},
publisher = {Elsevier},
keywords = {air quality prediction, deep learning, machine learning},
pubstate = {published},
tppubtype = {inbook}
}
2021
Sánchez-Pozo, Nadia N; Trilles-Oliver, Sergio; Solé-Ribalta, Albert; Lorente-Leyva, Leandro L.; Mayorca-Torres, Dagoberto; Peluffo-Ordóñez, Diego H
Algorithms Air Quality Estimation: A Comparative Study of Stochastic and Heuristic Predictive Proceedings Article
In: Hybrid Artificial Intelligent Systems (International Conference on Hybrid Artificial Intelligence Systems), pp. 293-304, Springer, Cham, 2021, ISBN: 978-3-030-86271-8.
Abstract | Links | BibTeX | Tags: air quality prediction, machine learning
@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}
}
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.
Iskandaryan, Ditsuhi; Ramos-Romero, Francisco; Trilles-Oliver, Sergio
Features Exploration from Datasets Vision in Air Quality Prediction Domain Journal Article
In: Atmosphere, vol. 12, no. 3, pp. 312, 2021, ISSN: 2073-4433.
Abstract | Links | BibTeX | Tags: air quality prediction, data, machine learning
@article{Iskandaryan2021a,
title = {Features Exploration from Datasets Vision in Air Quality Prediction Domain},
author = {Ditsuhi Iskandaryan and Francisco Ramos-Romero and Sergio Trilles-Oliver},
doi = {https://doi.org/10.3390/atmos12030312},
issn = {2073-4433},
year = {2021},
date = {2021-02-28},
journal = {Atmosphere},
volume = {12},
number = {3},
pages = {312},
abstract = {Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.},
keywords = {air quality prediction, data, machine learning},
pubstate = {published},
tppubtype = {article}
}
Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.