Hybrid interpretable predictive machine learning model for air pollution prediction

Gu, Yu; Li, Baihua; Meng, Qinggang

doi:10.1016/j.neucom.2021.09.051

Cited by 56 publications

(24 citation statements)

References 43 publications

(75 reference statements)

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“…Correlation analysis was performed between the pollutant concentrations and the predictor variables and between each pair of predictor variables, respectively. The predictor variables with a lower correlation coefficient within paired predictors whose correlation coefficients were greater than 0.70 were excluded to mitigate the multicollinearity problem that could lead to overfitting. , All of the predictors were scaled and centered before being fed into the models. All of the co-located PM 10 and PM 2.5 data sets were used as the development set (see more details in Text S4 of the Supporting Information).…”

Section: Methodsmentioning

confidence: 99%

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Liu,

Ma,

Gasparrini

et al. 2023

Environ. Sci. Technol.

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Section: Methodsmentioning

confidence: 99%

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Liu,

Ma,

Gasparrini

et al. 2023

Environ. Sci. Technol.

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“…During the presented method, the LSTM technique has an analyst engine for predicting the count of produced NO2 and SO2 by the Combined Cycle Power Plant, whereas the MVO system was utilized for optimizing the LSTM parameter for achieving a lesser predictive error. Gu et al [12] examine a novel Hybrid Interpretable Predictive ML approach for Particulate Matter 2.5 predictive that takes 2 novelties. Primarily, a hybrid method infrastructure was generated with DNN and Non-linear Auto Regressive Moving averages with an Exogenous Input method.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Prediction using Ensemble Voting based Deep Learning with Mud Ring Algorithm for Intelligent Transportation Systems

2023

Global NEST: The International Journal

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<p>In recent times, advanced technologies in transportation are developing like connected and automated vehicles and shared mobility services. A rapidly increasing number of vehicles in intelligent transportation system (ITS) and smart cities causes pollution and degrade the quality of air. Owing to the incredible effect of air quality on individual lives, it is indispensable to design a system by which air pollutants (PM2.5, NOx, COx, SOx) are predicted. But predicting air quality and its pollutants was complex since air quality relies on various elements like power plants, weather, and vehicular emissions. Deep learning (DL) and Machine learning (ML) techniques are leveraged for developing an air quality predictive method. This study develops an Air Quality Prediction utilizing Ensemble Voting based Deep Learning with Mud Ring Algorithm (AQP-EDLMRA) technique. The presented AQP-EDLMRA technique follows the ensemble voting model, which exploits three DL classification methods like long short-term memory (LSTM), deep belief network (DBN), and stacked autoencoder (SAE). Then, the new data can be classified by the weighted vote of their prediction outcomes. To adjust the hyperparameter values of the DL methods, the MRA was exploited, showing the novelty of the work. The simulation values of the AQP-EDLMRA approach are tested using a series of air quality data and the comprehensive comparative results demonstrated that the AQP-EDLMRA technique has reached improved forecasting performance.</p>

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“…These methods are based on the observation data, and parameter estimation is established via curve fitting and pre-defined mathematical conditions [ 8 ]. Donnelly and Xiao et al propose several linear statistical models for the prediction of particulate concentrations [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin

Zhao

et al. 2022

Entropy

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Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial–temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial–temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win–tie–loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.

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Hybrid interpretable predictive machine learning model for air pollution prediction

Cited by 56 publications

References 43 publications

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Air Quality Prediction using Ensemble Voting based Deep Learning with Mud Ring Algorithm for Intelligent Transportation Systems

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

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