Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

Peng, Huiping; Lima, Aranildo R.; Teakles, Andrew; Jin, Jian; Cannon, Alex J.; Hsieh, William W.

doi:10.1007/s11869-016-0414-3

Cited by 57 publications

(44 citation statements)

References 33 publications

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“…The EEF method can be useful to meet the computational power constraints for the continual arrival of new data, which necessitates frequent model updating in atmospheric science. Peng et al [24] note that computational expense is one of the difficulties in air quality forecasting. Although the results of this study indicated the efficiency of the proposed framework in application to synthetic data simulation, further evaluations of the proposed framework are still necessary, especially in applications to data assimilation problems with real data and numerous observations.…”

Section: Resultsmentioning

confidence: 99%

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Foroozand

Weijs

2017

Entropy

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Over the past two decades, the Bootstrap AGGregatING (bagging) method has been widely used for improving simulation. The computational cost of this method scales with the size of the ensemble, but excessively reducing the ensemble size comes at the cost of reduced predictive performance. The novel procedure proposed in this study is the Entropy Ensemble Filter (EEF), which uses the most informative training data sets in the ensemble rather than all ensemble members created by the bagging method. The results of this study indicate efficiency of the proposed method in application to synthetic data simulation on a sinusoidal signal, a sawtooth signal, and a composite signal. The EEF method can reduce the computational time of simulation by around 50% on average while maintaining predictive performance at the same level of the conventional method, where all of the ensemble models are used for simulation. The analysis of the error gradient (root mean square error of ensemble averages) shows that using the 40% most informative ensemble members of the set initially defined by the user appears to be most effective.

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Section: Resultsmentioning

confidence: 99%

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Foroozand

Weijs

2017

Entropy

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“…It appears that the ELM is significantly better than NN and Multiple Linear Regression (MLR), because it provides a higher accuracy and a lower computational cost. An even more advanced study is carried by Peng et al [62]. The authors propose an efficient non-linear machine learning algorithm for air quality forecasting (up to 48 h), which is easily updatable in 'real time' thanks to a linear solution applied to the new data.…”

Section: Category 4: Hybrid Models and Extreme/deep Learningmentioning

confidence: 99%

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018

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Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

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“…Meteorological parameters refer to the variables that characterise atmospheric chemistry. Meteorological variables especially wind speed, wind direction, relative humidity and atmospheric turbulence have been found to have a massive influence on the dispersion and concentration of several air pollutants including O3, NO2, PM10 and PM2.5 (Colls, 2001;Dominick et al, 2012;Kumar et al, 2017;Peng et al, 2017). Emissions data primarily refers to primary and secondary air pollutants in urban environments.…”

Section: Introductionmentioning

confidence: 99%

A review of artificial neural network models for ambient air pollution prediction

Cabaneros

Calautit

Hughes

2019

Environmental Modelling & Software

329

134

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Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models.

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Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods

Cited by 57 publications

References 33 publications

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

A review of artificial neural network models for ambient air pollution prediction

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