Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

Filho, Francisco S. de Albuquerque; Madeiro, Francisco; Fernandes, Stênio; Neto, Paulo S. G. de Mattos; Ferreira, Tiago A. E.

doi:10.1590/s0100-40422013000600007

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…In this work, we used four different neural models in the framework depicted in Figure 1: multilayer perceptron (MLP), radial basis function network (RBF), extreme learning machines (ELM), and echo state networks. These models were selected due to their extensive use in time series forecasting [8][9][10][11][12][13][14][15][16]28,46,[54][55][56]. These ANN architectures can approximate any nonlinear, continuous, limited, and differentiable function, being universal approximators [57].…”

Section: Forecasting Models Used In the Proposed Approachmentioning

confidence: 99%

“…In this context, we highlight the forecasting systems based on artificial neural networks (ANNs), due to the excellent performances demonstrated in the literature [8][9][10][11][12][13][14][15]. These systems generally employ two data-driven approaches for ANN adjustment: the use of only previous (historical) data of PM concentration [12,13,16,17] or the use of the historical data of PM jointly with related features, such as temperature, relative humidity, wind speed and direction, and others [8][9][10][11]14,[18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, systems based on ANN use only one forecasting model for the entire series [8][9][10][12][13][14][15][16]. However, some works [19][20][21][22][23][24][25][26][27][28] address a set of predictors for a specific part of the series.…”

Section: Introductionmentioning

confidence: 99%

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A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

et al. 2020

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Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities.

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Section: Forecasting Models Used In the Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

et al. 2020

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“…In addition, a model considering various variables was proposed by combining ensemble learning and multi-layer perceptron (MLP) [ 16 , 17 , 18 , 19 , 20 ]. Many diverse studies have been conducted, in which particle swarms have been optimized using swarm intelligence in combination with ANNs [ 21 , 22 , 23 , 24 , 25 ]. In the field of residual modeling, multiple studies have been conducted on hybrid models, in order to improve the performance of predicting PM concentration levels [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Park

Chang

2021

IJERPH

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Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

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“…[1][2][3] Las partículas atmosféricas han cobrado recientemente un notable interés debido a los efectos adversos generados en la salud del ser humano, principalmente aquellas con diámetro aerodinámico menor que 10 µm y 2.5 µm. Tanto la Agencia de Protección al Ambiente (US-EPA), y la Organización Mundial de la Salud (WHO), advierten la importancia de la medición de este contaminante en el aire, debido a los efectos adversos en las poblaciones humanas.…”

Section: Introductionunclassified

Modelado De Partículas Pm10 Y PM2.5 Mediante Redes Neuronales Artificiales Sobre Clima Tropical De San Francisco De Campeche, México

et al. 2017

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Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

Cited by 18 publications

References 19 publications

A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Modelado De Partículas Pm10 Y PM2.5 Mediante Redes Neuronales Artificiales Sobre Clima Tropical De San Francisco De Campeche, México

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