Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong

Lu, Wen-Hsiang; Wang, Wenjian; Wang, Xiekang; Yan, Sui-Hang; Lam, Joseph C.

doi:10.1016/j.envres.2003.11.003

Cited by 73 publications

(45 citation statements)

References 26 publications

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“…ANN are especially useful in forecasting pollution level in cities, where complexity of problem preclude different methods of modeling. Many works with similar approach can be stated [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the prediction models of the air quality state in cities

Skrzypski¹,

Jach-Szakiel²,

Kami³

et al. 2007

WIT Transactions on Ecology and the Environment, Vol 101

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The aim of research was to optimize the neural networks models for predicting the classes of air quality state. This model was constructed and tested on the basis of the data gathered in Lodz, a city localized in the middle of Poland. Models were tested in relation to mean daily dust concentration (PM 10 ) as well as maximal daily values. In each case, 5 air quality classes were distinguished. Air quality in each day was classified with respect to the meteorological conditions. Two models were built: two artificial neural networks (ANNs) were used, namely the MLP and the RBF. Optimization relied on determining the value of the following optimal value of the following ratio of the training set to the testing set, neuron number, number of input vectors after the PCA dimension reduction procedure.Results of the modelling are satisfactory. The error for predicting the air quality classes was generally smaller than 13%. In relation to predicting air quality classes, mean daily PM 10 concentrations, better results were obtained with the RBF model containing 5 neurons. The RBF model for maximal daily PM 10 concentrations generates a classification error of about 10,7%, and MLP model generates an error of 14,9%.

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Section: Introductionmentioning

confidence: 99%

Optimizing the prediction models of the air quality state in cities

Skrzypski¹,

Jach-Szakiel²,

Kami³

et al. 2007

WIT Transactions on Ecology and the Environment, Vol 101

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show abstract

“…The PCA approach is used in the proposed prediction model to simplify and orthogonalize the original data set in order to optimize the models because a small set of uncorrelated variables is easier to understand and use in further analyses than is a larger set of correlated variables [22].…”

Section: Pca Approachmentioning

confidence: 99%

“…Second, because a large number of attributes would be employed as model inputs, this study developed an improved approach that combines the principal component analysis (PCA) technique and data mining models. Some researchers, such as [21][22][23][24], concluded that PCA can be employed to find a A useful scheme for nearshore wave forecasts during typhoons is highly desirable in Taiwan. Data mining, which has popularized data-driven models, has been in widespread use in coastal and oceanic applications.…”

Section: Introductionmentioning

confidence: 99%

“…Second, because a large number of attributes would be employed as model inputs, this study developed an improved approach that combines the principal component analysis (PCA) technique and data mining models. Some researchers, such as [21][22][23][24], concluded that PCA can be employed to find a set of orthogonal components that minimize the error in the reconstructed data. This paper presents popular data mining techniques, including k-nearest neighbors (kNN), LR, M5, multilayer perceptron (MLP) neural network, and support vector regressions (SVR) that are often used for ensemble forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

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Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Wei

2017

Energies

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Seasonal typhoons provide energy into the wave field in summer and autumn in Taiwan. Typhoons lead to abundant wave energy near the coastal area and cause storm surges that can destroy offshore facilities. The potential for wave energy can be obtained from analyzing the wave height. To develop an effective model for predicting typhoon-induced wave height near coastal areas, this study employed various popular data mining models-namely k-nearest neighbors (kNN), linear regressions (LR), model trees (M5), multilayer perceptron (MLP) neural network, and support vector regression (SVR) algorithms-as forecasting techniques. The principal component analysis (PCA) was then performed to reduce the potential variables from the original data at the first stage of data preprocessing. The experimental site was the Longdong buoy off the northeastern coast of Taiwan. Data on typhoons that occurred during 2002-2011 and 2012-2013 were collected for training and testing, respectively. This study designed four PCA cases, namely EV1, TV90, TV95, and ORI: EV1 used eigenvalues higher than 1.0 as principal components; TV90 and TV95 used the total variance percentages of 90% and 95%, respectively; and ORI used the original data. The forecast horizons varying from 1 h to 6 h were evaluated. The results show that (1) in the PCA model' cases, when the number of attributes decreases, computing time decreases and prediction error increases; (2) regarding classified wave heights, M5 provides excellent outcomes at the small wavelet wavelet level; MLP has favorable outcomes at the large wavelet and small/moderate wave levels; meanwhile, SVR gives optimal outcomes at the long wave and high/very high wave levels; and (3) for performance of lead times, MLP and SVR achieve more favorable relative weighted performance without consideration of computational complexity; however, MLP and SVR might obtain lower performance when computational complexity is considered.

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“…ANNs were used for description and prediction of the air pollution concentration fields in cities [3][4][5][6][7][8].…”

Section: Territory and Scope Of Researchmentioning

confidence: 99%

Application of artificial neural networks for classification and prediction of air quality classes

Skrzypski¹,

Kami²,

ski³

et al. 2009

WIT Transactions on Ecology and the Environment

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In this study, the results of investigations which enable an extension of the mathematical methods supporting air quality management in cities are presented. The actions were focused on the development of neural models of classification and prediction of the air quality classes (in respect of PM10 dust concentrations). The air quality class on a following day was predicted. The aim of modelling was to predict the air quality classes in the afternoon and in the evening when PM10 concentrations attained the daily maxima. The monitoring of PM10 concentration and the meteorological data for winter periods in 2004-2007 was used. The artificial neural network methods (ANN) with a simultaneous application of data compression methods were tested. The results of the air quality prediction are satisfactory. The accurate prognoses are predominant. The percentage of wrong prognoses is relatively small. The investigations confirm that neural prediction models allow good results to be obtained of the air quality class prediction. The results of the research prove that the tested models may be applied in the practice of air quality management in cities.

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Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong

Cited by 73 publications

References 26 publications

Optimizing the prediction models of the air quality state in cities

Optimizing the prediction models of the air quality state in cities

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Application of artificial neural networks for classification and prediction of air quality classes

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