PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

Dong, Ming; Yang, Dong; Kuang, Yan; He, David; Erdal, Serap; Kenski, Donna

doi:10.1016/j.eswa.2008.12.017

Cited by 123 publications

(58 citation statements)

References 34 publications

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“…Various panniers of explicative variables have been used in the previous works for the purpose of PM 10 modeling and forecasting (Dong et al 2009;Kurt and Oktay 2010;Poggi and Portier 2011;Domańska and Wojtylak 2012). This variety depends on the availability of measured variables and the objectives of the study.…”

Section: Data Gathering and Descriptionmentioning

confidence: 98%

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

2016

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Over the past years, the health impact of airborne particulate matter PM 10 has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient PM 10 concentrations. In this paper, we are interested in the statistical classification of the daily mean PM 10 concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of PM 10 concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the PM 10 concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of PM 10 concentration may be crucial to get an accurate prediction.

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Section: Data Gathering and Descriptionmentioning

confidence: 98%

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

2016

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“…Various panniers of explicative variables have been used in the previous works for the purpose of PM10 modeling and forecasting (Dong et al, 2009;Kurt and Oktay, 2010;Poggi and Portier, 2011;Domańska and Wojtylak, 2012). This variety depends on the availability of measured variables and the objectives of the study.…”

Section: Data Gathering and Descriptionmentioning

confidence: 99%

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

Ishak¹

2016

J ENV INFORM

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ABSTRACT. In this work, the problem of variable selection for regression is investigated in order to improve the forecasting accuracy. To that effect, the support vector regression (SVR) and the random forests (RF) are used to assess the variable importance. Then, a stepwise algorithm is built to select the best subset of predictors. An intensive comparative study is conducted on simulated and real datasets. The real datasets expose the problem of particulate matter concentration forecasting in two monitoring stations from Tunisia. We have proposed a combined approach using SVR and RF for variable importance assessment and for variable selection. We have achieved a significant improvement in forecasts accuracy for the two stations when using only a reduced number of selected predictors.

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“…The screened samples should conform to the following formula: n Q num ≤ (6) where Q num is the number of samples in the sequenced sample column, and n is the number of samples needed.…”

Section: The Basic Descriptionmentioning

confidence: 99%

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

et al. 2015

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Abstract:In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO 2 , NO 2 , and PM 10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO 2 , NO 2 , and PM 10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM 10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R 2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. OPEN ACCESSAtmosphere 2015, 6 892

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PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

Cited by 123 publications

References 34 publications

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

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