Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

Elangasinghe, M.A.; Singhal, Naresh; Dirks, Kim N.; Salmond, Jennifer; Samarasinghe, Sandhya

doi:10.1016/j.atmosenv.2014.04.051

Cited by 142 publications

(76 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…So, this paper confirms the Gardner and Dorling 2000; Chaloulakou et al 2003b;Grivas and Chaloulakou 2006;Palani et al 2008;Elangasinghe et al 2014 studies in new geographic location. The fluctuation of NO 2 and NO x concentrations in this work could be influenced by local meteorological factors such as relative humidity and temperature.…”

Section: Discussionsupporting

confidence: 69%

“…The findings of numerous research studies also exhibit that the performance of ANNs is generally superior in comparison to traditional statistical methods, such as multiple regression, classification and regression trees, and autoregressive models (Gardner and Dorling 2000;Chaloulakou et al 2003a;Grivas and Chaloulakou 2006;Palani et al 2008;Elangasinghe et al 2014). In this paper, we used ANN for forecasting air pollution in new geographic location (Tabriz) with deferent climate condition to confirm previous studies.…”

Section: Introductionsupporting

confidence: 61%

“…It can be used to derive nonlinear functions relating the concentrations of pollutants to meteorological and source characteristics. The accuracy of the trained neural network model in predicting the concentrations of an unseen data set reflect the extent to which the input parameters have captured the emission and dispersion pattern that have resulted from the observed concentrations (Elangasinghe et al 2014). ANNs can model nonlinear systems and have been employed for modeling of systems (Chelani et al 2002).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Rahimi

2017

Ecol Process

View full text Add to dashboard Cite

Introduction: Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. Methods: The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO 2 and NO x concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NO x and NO 2 concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Results: Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R 2 ) values are 0.92 and 0/94 for NO 2 and NO x concentrations, respectively. But in MLR model, R 2 values were 0.41 and 0.44 for NO 2 and NO x concentrations, respectively. Conclusions: This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO 2 and NO x concentrations in an urban environment compared to linear models.

show abstract

Section: Discussionsupporting

confidence: 69%

Section: Introductionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Rahimi

2017

Ecol Process

View full text Add to dashboard Cite

show abstract

“…The data used were from Auckland New Zealand s most populated city with a population of over 1.4 million. It was found that the inclusion of cluster rankings, derived from k-means cluster analysis, as an input parameter to the ANN model showed a statistically signiicant improvement in the performance of the ANN model and that the model was also beter at predicting high concentrations [62,63].…”

Section: New Zealand Pm Trends and Modelsmentioning

confidence: 99%

Particulate Matter Sampling Techniques and Data Modelling Methods

Whalley¹,

Zandi²

2016

Air Quality - Measurement and Modeling

View full text Add to dashboard Cite

Particulate mater with 10 μm or less in diameter PM 10 ) is known to have adverse efects on human health and the environment. For countries commited to reducing PM 10 emissions, it is essential to have models that accurately estimate and predict PM 10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM 10 is presented. This includes the instrumentation used to measure particulate mater, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM 10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM 10 trends in New Zealand a remote country of islands in the South Paciic Ocean are examined. In conclusion, the issues and challenges faced when modelling PM 10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM 10 prediction and estimation models are presented.

show abstract

“…The authors used a MLP and the meteorological and AQ pollutants to predict the next day mean concentration of PM 10 and PM 2.5 . A genetically optimized ANN and k-means clustering was applied in [8] to predict PM 10 and PM 2.5 in a coastal location of New Zealand. A hybrid PM 2.5 forecasting model that uses feed forward ANN combined with rolling mechanism and accumulated generating operation of gray model, that was experimented in three cities from China is introduced in [11].…”

Section: An Overview On Pm 25 Computational Intelligence Based Forecmentioning

confidence: 99%

Computational Intelligence-based PM2.5 Air Pollution Forecasting

Oprea¹,

Mihalache²,

Popescu³

2017

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM 2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27] a , in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM 2.5 ) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM 2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.

show abstract

Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

Cited by 142 publications

References 18 publications

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Particulate Matter Sampling Techniques and Data Modelling Methods

Computational Intelligence-based PM2.5 Air Pollution Forecasting

Contact Info

Product

Resources

About