A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors

Wu, Qunli; Lin, Huaxing

doi:10.1016/j.scitotenv.2019.05.288

Cited by 159 publications

(50 citation statements)

References 44 publications

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“…A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors [65]: Wu and Lin suggested optimal-hybrid model combined with Secondary Decomposition (SD), AI method and optimization algorithm for forecasting air quality index. In the proposed SD method, Wavelet Decomposition (WD) was chosen as the primary decomposition technique to generate a high-frequency detail sequence WD (D) and a low-frequency approximation sequence WD (A).…”

Section: Group 4: Hybrid Modelmentioning

confidence: 99%

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

2020

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The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

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Section: Group 4: Hybrid Modelmentioning

confidence: 99%

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

2020

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“…Aiming at the problem of data correction of the miniature air quality detector, we proposed a combined air quality prediction model 35,36 based on principal component regression, support vector regression and autoregressive moving average model. The PCR-SVR-ARMA model was successfully applied in the calibration data of the miniature air quality detector.…”

Section: Discussionmentioning

confidence: 99%

Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model

Liu

Jin

2021

Sci Rep

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In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.

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“…Compared with the traditional decomposition methods, VMD has stronger decomposition ability and anti noise interference ability, and its operation speed is faster [32], [33]. Wu et al [34] combined VMD with prediction method to forecast AQI . Li et al [35] proposed a forecasting model of sunspot number based on the combination of VMD and BP neural network.…”

Section: Introductionmentioning

confidence: 99%

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

Chang

Yang

2020

IEEE Access

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Precipitation is an important parameter of water resource management, flood warning and hydrological analysis, so it is important to predict rainfall accurately. However, many previous studies did not extract the information of error series and only used a single model to predict rainfall data, ignoring the importance of model stability. Therefore, based on the idea of combination prediction and error correction strategy, this paper proposes a novel combined prediction model for monthly mean precipitation. It combines the variational mode decomposition (VMD), the improved butterfly optimization algorithm (IBOA), the least squares support vector machine model (LSSVM), the adaptive Volterra and autoregressive moving average (ARMA) model. Firstly, in order to find the best parameters of LSSVM, an improved butterfly optimization algorithm is proposed. The simulation results show the performance of IBOA is better than that of other algorithms, such as PSO, DE and BOA. Then the IBOA-LSSVM model and Volterra model are established for the mode components of the VMD, named VMD-IBOA-LSSVM and VMD-Volterra. Secondly, to solve the problem that the uncertainty of the hydrological prediction model, a combined precipitation prediction method based on the induced ordered weighted average (IOWA) operator of VMD-IBOA-LSSVM and VMD-Volterra is proposed. Finally, the ARMA model is established to correct the error sequence of the combined forecasting model. The precipitation data of two stations in Shaanxi Province are predicted. Experiment 1 is taken as an example, the maximum error of the proposed prediction model for rainfall is less than 9 mm, and the performance of the proposed model is improved by at least 43%. It shows that the proposed model can effectively reduce the prediction error of precipitation, and provide a new idea for precipitation prediction.

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A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors

Cited by 159 publications

References 44 publications

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

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