A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area

Wongsathan, Rati; Seedadan, Isaravuth

doi:10.1016/j.procs.2016.05.057

Cited by 32 publications

(13 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The high performance of hybrid GASVR may due to the fact that the PM10 concentration time series data probably consist of both complex linear and non-linear patterns. It has been known that ARIMA model is only good at capturing linear patterns [4], [6], [7]. On the contrary, the GASVR that is based on the machine learning technique tends to better capturing non-linear patterns than the linear patterns [7], [8].…”

Section: A Comparison Between the Hybrid Gasvr And The Arima Modelsmentioning

confidence: 99%

“…Wongsathan and Seedadan [4] studied and developed the hybrid model that combined the ARIMA model with the neural networks (NNs) model to forecast the PM10 in Chiang Mai city moat area of Thailand. The errors of the ARIMA model were used to generate the NNs model.…”

Section: Introductionmentioning

confidence: 99%

“…ARIMA was a popular forecasting model to predict a univariate time series due to its high accuracy [5], [6]. The ARIMA model is good at capturing linear patterns, but it cannot easily capture the non-linear pattern [4], [6], [7]. For The Forecast of PM10 Pollutant by Using a Hybrid Model Ronnachai Chuentawat, Nittaya Kerdprasop, and Kittisak Kerdprasop capturing non-linear patterns, the Support Vector Regression (SVR) model, developed from the machine learning field, can do a better job than ARIMA.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Forecast of PM10 Pollutant by Using a Hybrid Model

Chuentawat¹,

Kerdprasop²,

Kerdprasop³

2017

IJFCC

View full text Add to dashboard Cite

Section: A Comparison Between the Hybrid Gasvr And The Arima Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Forecast of PM10 Pollutant by Using a Hybrid Model

Chuentawat¹,

Kerdprasop²,

Kerdprasop³

2017

IJFCC

View full text Add to dashboard Cite

“…Similar research in [9] also discussed the respirable suspended particulate matter (RSPM) modeling by using ARIMA and SARIMA for the Pune city India. Nonparametric modeling also has been used as in [10] which has developed an artificial neural network (ANN) model for forecasting respirable suspended particulate matter (RSPM) concentration in a major urban area Pune Maharashtra, whereas hybrid NN-ARIMA has been conducted [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm and Conjugate Gradient Optimization in Neural Network for Prediction of Suspended Particulate Matter

Warsito¹,

Prahutama²,

Yasin³

et al. 2019

E3S Web Conf.

View full text Add to dashboard Cite

The scope of this research is the use of artificial neural network models and meta-heuristic optimization of Particle Swarm Optimization (PSO) for the prediction of ambient air pollution parameter data at air quality monitoring stations in the city of Semarang, Central Java. The observed parameter is an indicator of ambient air quality, Suspended Particulate Matter (SPM). Based on air quality parameter data in previous times which is a time series data, modeling is done using Neural Networks (NN). Estimation of weights from NN is done using a hybrid method between meta-heuristic and gradient optimization. The meta-heuristic optimization method used is Particle Swarm Optimization (PSO) while the gradient based method is the Conjugate Gradient. Optimization with PSO is done first, then proceed with optimization using the Conjugate Gradient. Four scenarios of iteration selection at the PSO stage are 10, 25, 50 and 100. At the Conjugate Gradient, stage iteration is carried out up to 1000 epohs. The predicted results were compared with the PSOs and Conjugate Gradient respectively. The results show that the hybrid method provides better predictions. The number of iterations needed at the PSO stage is not too much so it is efficient in combining the two methods.

show abstract

“…Accordingly, compared to empirical models, CTMs is less accurate. Empirical models mainly involve multiple linear regression (MLR), autoregressive integrated moving average model (ARIMA), hidden Markov model and artificial intelligence models, which are generally applied in air pollutant forecasting [5,7,8,9]. However, the most prevalent model for air pollutant forecasting is based on the theory of artificial intelligence, which is efficient and accurate in practical application.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Wang

Niu

Wang

2017

IJERPH

View full text Add to dashboard Cite

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

show abstract

A Hybrid ARIMA and Neural Networks Model for PM-10 Pollution Estimation: The Case of Chiang Mai City Moat Area

Cited by 32 publications

References 4 publications

The Forecast of PM10 Pollutant by Using a Hybrid Model

The Forecast of PM10 Pollutant by Using a Hybrid Model

Hybrid Particle Swarm and Conjugate Gradient Optimization in Neural Network for Prediction of Suspended Particulate Matter

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Contact Info

Product

Resources

About