Recursive neural network model for analysis and forecast of PM10 and PM2.5

Biancofiore, Fabio; Busilacchio, Marcella; Verdecchia, Marco; Tomassetti, Barbara; Aruffo, Eleonora; Bianco, Sebastiano; Tommaso, Sinibaldo Di; Colangeli, Carlo; Rosatelli, Gianluigi; Carlo, Piero Di

doi:10.1016/j.apr.2016.12.014

Cited by 254 publications

(143 citation statements)

References 32 publications

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“…6b, it can be observed that there is a strong correlation between observed values and predicted ones. The results of the study were comparable with previously reported on prediction of various pollutant concentrations (Srimuruganandam and Shiva Nagendra 2010; Biancofiore et al 2017;Ozel and Cakmakyapan 2015;Auder et al 2016b) . Although its non-linear and complex structure, multiple linear regression models assume a linear relationship between meteorological variables and PM10 concentration.…”

Section: Prediction Using Annsupporting

confidence: 82%

“…Artificial Neural Networks (ANN) is one of the widely applied artificial intelligence models in different research areas for prediction (Elangasinghe et al, 2014;Russo et al, 2015;Fang and Wang 2017;Biancofiore et al 2017;Özgür and Tosun 2017;Altıner and Kuvvetli, 2017;Sofuoglu et al 2006). ANN models have approved to be convenient way for estimation air pollutants in cities, especially where monitoring networks are used to measure concentrations of pollutants and meteorological variables (Fang and Wang, 2017;Sofuoglu et al, 2006;Özgür and Tosun, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

2018

Global NEST Journal

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In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels.

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Section: Prediction Using Annsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

2018

Global NEST Journal

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show abstract

“…Artificial neural networks (ANN) comprise a mathematical model based on a collection of artificial neurons that are connected or functionally-related to each other. Each of the nodes of ANN models behaves like neurons in a biological brain [95,96]. Overall, the neurons of ANN models are arranged within an input layer, hidden layers, and an output layer (Figure 8).…”

Section: Introductionmentioning

confidence: 99%

“…All neurons in a layer are linked to all neurons in neighbor layers by synaptic weights playing the role of signaling coefficients to the corresponding connections [95][96][97]. These connecting signals are real numbers, and the output of each of the artificial neurons is analyzed by many linear or non-linear statistical techniques [95,98]. The multilayer Perceptron (MLP) neural network and the radial basis function (RBF) network are two of the most popular neural networks [98].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

et al. 2020

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The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches' many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.Atmosphere 2020, 11, 137 2 of 27 PM Cd , and PM Pb ), the annual limit values are 6, 5, and 500 ng/m 3 , respectively (Directives 1999/30/EC and 2008/50/EC) [9]. Inhaled drug therapy can be a useful option against many lung diseases such as asthma or COPD. The efficiency of pharmaceutical aerosols depends on their deposition in the airways, especially the upper generations [10]. The estimation of aerosol transportation and deposition in the human lung can therefore indicate strategies for the prevention or alleviation of health effects from inhaled toxic particles or new drug-aerosol delivery approaches that can overcome the limitations of inhalers [11]. Atmospheric-aerosol deposition in the human respiratory tract (RT) can be directly measured by monitoring and comparing inhaled and exhaled particle concentrations. However, due to experimental limitations, the regional dose in the respiratory system is hard to measure experimentally but can be predicted by the application of different mathematical models. Suitable mathematical models are those that show a reasonable correlation between results obtained from modeling and empirical measurements [12]. Computational models generally consist of three elements: (1) a mathematical formulation that describes the relevant physical and chemical processes, (2) the setting of specific initial and boundary conditions, and (3) the solving of equations for the specified geometry. The accuracy of a computational model depends on the extent to which these elements are realistic. Additionally, regardless of the computational model that is applied, four types of input data are required: (1) lung morphometry, (2) breathing pattern, (3) particle properties, and (4) gas/vapor properties (Figure 1) [4]. The effects of these input data to the computational models were introduced in the review of Rostami (2009) and Hussain et al. (2011) [4,13]. In this review, we only present the advances of lung geometry models due to their strong relations to the accuracies of the computational models [4,13].

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“…In addition, we make use of an ensemble approach based on an ANN model of simple architecture which can be applied to multiple geographic areas, thus simplifying the ensemble approach suggested by [14] and [15], while maintaining a performance comparable to the one reported by similar studies [16], and therefore providing with a novel approach to the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

Revisiting urban air quality forecasting: a regression approach

Karatzas

Katsifarakis

Orłowski

et al. 2018

Vietnam J Comput Sci

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We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean PM 10 concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.

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Recursive neural network model for analysis and forecast of PM10 and PM2.5

Cited by 254 publications

References 32 publications

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

Revisiting urban air quality forecasting: a regression approach

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