Reliable prediction of carbon monoxide using developed support vector machine

Moazami, Saber; Noori, Roohollah; Amiri, Bahman Jabbarian; Yeganeh, Bijan; Partani, Sadegh; Safavi, Salman

doi:10.1016/j.apr.2015.10.022

Cited by 57 publications

(18 citation statements)

References 36 publications

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“…This is done using weights and uses its own transfer function to create an output value (Bai et al, 2016a;Moazami et. al., 2016).…”

Section: Ann Modelmentioning

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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“…This is done using weights and uses its own transfer function to create an output value (Bai et al, 2016a;Moazami et. al., 2016).…”

Section: Ann Modelmentioning

confidence: 99%

“…(Taşpinar, 2015;Russo et al, 2015;Mishra, Goyal and Upadhyay, 2015b;Guadalupe et al, 2015;Elangasinghe et al, 2014;Özdemir and Taner, 2014;Caselli et al, 2009;Moazami et al, 2016;Bai et al, 2016). Perez and Reyes, (2006), established a neural network to estimate level of average PM10 concentration on the next day in Santiago, Chile.…”

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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“…Classification by SVM SVMs are a class of supervised-learning-based models that classify input data sets by mapping into highdimensional feature spaces. While SVMs have been used since 1995 (Cortes and Vapnik, 1995), they have only relatively recently been applied to air pollution concentration predictions (Lu and Wang, 2005;Luna et al, 2014;Moazami et al, 2016). A full description of the SVM is beyond the scope of this paper, but several excellent references and online tutorials are available.…”

Section: Validation Of Methodologiesmentioning

confidence: 99%

Mapping air quality zones for coastal urban centers

Freeman

Gharabaghi

Munshed

et al. 2017

Journal of the Air & Waste Management Association

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“…This model can be used as an effective air quality management tool based on various scenarios. Moazami et al [23] developed a methodology based on uncertainty analysis by using a support vector machine for regression (SVR) model to predict the next day's concentration of CO. Different datasets were trained to find a suitable dataset to be used in the proposed methodology, which involved weather information and background pollutants such as CH4 and PM 10 .…”

Section: Previous Workmentioning

confidence: 99%

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018

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Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model's results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

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Reliable prediction of carbon monoxide using developed support vector machine

Cited by 57 publications

References 36 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

Mapping air quality zones for coastal urban centers

Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

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