Evolving the neural network model for forecasting air pollution time series

Niska, Harri; Hiltunen, Teri; Karppinen, Ari; Ruuskanen, Juhani; Kolehmainen, Mikko

doi:10.1016/j.engappai.2004.02.002

Cited by 195 publications

(66 citation statements)

References 15 publications

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“…Although, designing neural networks for high efficiency is difficult, but Harry Niska et al have been used multi-layer perceptron model to anticipate Nitrogen Dioxide during day time in a crowded traffic station in Helsinki city by parallel combining of genetic algorithm in parallel to choose input and design high level architecture. It removed the practical problems of designing neural networks for high efficiency and indicated that combined networks can guarantee it [10].…”

Section: Predict Using Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Networks in Estimating Participation in Elections

Khaze¹,

Màsdàrí²,

Hojjatkhah³

2013

IJITMC

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Section: Predict Using Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Networks in Estimating Participation in Elections

Khaze¹,

Màsdàrí²,

Hojjatkhah³

2013

IJITMC

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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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“…GAs provide robust and reliable solutions for highly complex, nonlinear search and optimization problems, which previously could not be solved (Holland, 1995;Schwefel, 1995). The use of GAs has been increasing rapidly and they are mainly used in combination with other neural network models for extracting decision rules (Blackmore and Bossamaier, 2003) and for selecting the input parameters and defining the model structure for predicting atmospheric contaminants (Niska et al, 2004). …”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus

Kadiyala

Kaur

Kumar

2012

Journal of the Air & Waste Management Association

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The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO 2 ), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO 2 ), 0.3-0.4 µm sized particle numbers, 0.4-0.5 µm sized particle numbers, particulate matter (PM) concentrations less than 1.0 µm (PM 1.0 ), and PM concentrations less than 2.5 µm (PM 2.5 ) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH.First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based backpropagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks.Implications: The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

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Evolving the neural network model for forecasting air pollution time series

Cited by 195 publications

References 15 publications

Application of Artificial Neural Networks in Estimating Participation in Elections

Application of Artificial Neural Networks in Estimating Participation in Elections

Optimizing the prediction models of the air quality state in cities

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus

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