Hybrid intelligent system for air quality forecasting using phase adjustment

Neto, Paulo S. G. de Mattos; Madeiro, Francisco; Ferreira, Tiago A. E.; Cavalcanti, George D. C.

doi:10.1016/j.engappai.2014.03.010

Cited by 45 publications

(34 citation statements)

References 29 publications

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“…The lowest squared error on the verification is supposed to have the best generalization ability [37]. During the training of ANN models, the stopping criteria are the number of epochs and the decrease in the training error [38]. The model validation for the linear model, in the case for non-linear models, known as model testing, is aimed at measuring the agreement of the models, generalizing with an independent data set [39].…”

Section: Data Acquisitionmentioning

confidence: 99%

Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

et al. 2019

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Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000–2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM10 and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM10 compared to the linear model. The results are robust enough for precise next day forecasting of PM10 concentration on the East Coast of Peninsular Malaysia.

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Section: Data Acquisitionmentioning

confidence: 99%

Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

et al. 2019

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“…The evaluation of the proposed method is performed by six measures [17]: Mean Squared Error (MSE), Prediction of Change in Direction (POCID), U of Theil Statistics (U), Mean Absolute Percentage Error (MAPE), Average Relative Variance (ARV) and Index of Agreement (IA).This set of measures is used with objective to have different statistics and points of view of the performance of the proposed method [18].…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…The MSE, MAPE, U and ARV are commonly used in the literature of the time series forecasting as evaluation performance [17]. For each one of these measures, the lower the value, more accurate is the forecasting model.…”

Section: Evaluation Measuresmentioning

confidence: 99%

Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting

Neto

Ferreira

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultrahigh-frequency stock prices. The intelligent system is composed of a genetic algorithm (GA) that seeks the best parameters (the number of nodes in the input and hidden layers, and the training algorithm) of an artificial neural network (ANN) of type multiLayer perceptron (MLP). The proposed method called Time-delay Added Evolutionary Forecasting (TAEF) is a data-driven approach, that performs a pos-processing, where the objective is to reduce the difference between the forecasting and the actual series. The experimental study is performed using ultra-high frequency time series (Amazon, APPLE, Google and Intel stock prices) and shows that the proposed approach overcomes classical techniques of the computational intelligence and statistics in light of six relevant evaluation measures.

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“…There are nonlinear and complex interactions among variables of air quality prediction data. Artificial neural networks can be used as a nonlinear system to express complex nonlinear maps, so they have been frequently applied to realtime air quality forecasting (e.g., [1][2][3][4][5]).…”

Section: Introductionmentioning

confidence: 99%

A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

Shao

Sun

2019

Journal of Control Science and Engineering

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To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. In this paper, for the purpose of improve prediction accuracy of air pollutant concentration, a deep neural network model with multitask learning (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. MTL-DBN-DNN model can solve several related prediction tasks at the same time by using shared information contained in the training data of different tasks. In the model, DBN is used to learn feature representations. Each unit in the output layer is connected to only a subset of units in the last hidden layer of DBN. Such connection effectively avoids the problem that fully connected networks need to juggle the learning of each task while being trained, so that the trained networks cannot get optimal prediction accuracy for each task. The sliding window is used to take the recent data to dynamically adjust the parameters of the MTL-DBN-DNN model. The MTL-DBN-DNN model is evaluated with a dataset from Microsoft Research. Comparison with multiple baseline models shows that the proposed MTL-DBN-DNN achieve state-of-art performance on air pollutant concentration forecasting.

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Hybrid intelligent system for air quality forecasting using phase adjustment

Cited by 45 publications

References 29 publications

Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting

A DBN-Based Deep Neural Network Model with Multitask Learning for Online Air Quality Prediction

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