Prediction of PM10 concentrations through multi–gene genetic programming

Pires, José C.M.; Alvim-Ferraz, M. C. M.; Pereira, Maria do Carmo; Martins, F.G.

doi:10.5094/apr.2010.038

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Cai et al [18] compared the results obtained using a multilinear regression model to the ones achieved by an ANN when predicting hourly air pollutant concentration, concluding that ANNs produce more robust results. Pires et al [19] applied GP to predict the daily averages of PM 10 concentrations, comparing it with partial least square regression (PLSR). Tikhe Shruti [20] applied both ANNs and GP to the forecasting of air quality in India.…”

Section: Previous and Related Workmentioning

confidence: 99%

A Machine Learning Approach to Predict Air Quality in California

et al. 2020

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Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.

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Section: Previous and Related Workmentioning

confidence: 99%

A Machine Learning Approach to Predict Air Quality in California

et al. 2020

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“…Having a fixed frame rate at 10 fps (frame per second), ML light intensity versus time can be drawn for each time step (0. ) are incorporated into the multi-gene genetic programming (MGGP) toolbox [26,27] to develop a calibration equation of the change of effective strain e eff ( ) in each time step. The genetic programming (GP) is a widely known evolutionary algorithm that can produce encoded symbolic mathematical expressions from a large training data base.…”

Section: A Model From Uniaxial Tensile Reference Testmentioning

confidence: 99%

Quantitative full-field strain measurements by SAOED (SrAl₂O₄:Eu²⁺,Dy³⁺) mechanoluminescent materials

Azad

Rahimi²,

Yun

2016

Smart Mater. Struct.

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In this paper, a new calibration method for mechano-luminescence (ML) thin film sensors was proposed to enable quantitative full-field strain measurements in pixel-level resolution for the first time along with two standard reference test methods. The proposed method has a distinct advantage of its facet-free full-field strain sensing capability with pixel-level resolution. For the ML sensor, standard reference test methods were proposed for developing calibrated relationships between ML light intensity and effective strains: (1) uniaxial tensile reference test and (2) non-uniform strain reference test. From the reference tests, two different calibration models were developed in a recurrence equation form and validated measuring general strain distributions on different experimental specimens. Verified finite element (FE) simulation results were compared with ML effective strains to confirm its accuracy. The comparisons of the ML effective strains with FE simulation results showed that the calibration models can acceptably measure full-field strains. Limitations, sources of errors, suggestions for improving accuracy and practical considerations were also discussed. A conclusion of this research is that the proposed method enables ML sensing films to measure quantitative full-field strain distributions.

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“…e predictive models can be divided into two categories (single model and the hybrid model). Some scholars used a single model to study air quality, and the multigene genetic programming was used to predict the concentrations of PM 10 [7]. e grey Markov model was used to predict the concentration of air pollutants in Pingdingshan [8].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine

Deng

Chen

et al. 2020

Mathematical Problems in Engineering

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To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%.

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Prediction of PM10 concentrations through multi–gene genetic programming

Cited by 13 publications

References 10 publications

A Machine Learning Approach to Predict Air Quality in California

A Machine Learning Approach to Predict Air Quality in California

Quantitative full-field strain measurements by SAOED (SrAl₂O₄:Eu²⁺,Dy³⁺) mechanoluminescent materials

Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine

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Prediction of PM10 concentrations through multi–gene genetic programming

Cited by 13 publications

References 10 publications

A Machine Learning Approach to Predict Air Quality in California

A Machine Learning Approach to Predict Air Quality in California

Quantitative full-field strain measurements by SAOED (SrAl2O4:Eu2+,Dy3+) mechanoluminescent materials

Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine

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Product

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Quantitative full-field strain measurements by SAOED (SrAl₂O₄:Eu²⁺,Dy³⁺) mechanoluminescent materials