Adjusting prediction of ozone concentration based on CMAQ model and machine learning methods in Sichuan-Chongqing region, China

Lu, Hua; Xie, Min; Liu, Xiaoran; Liu, Bojun; Jiang, Min-Zhi; Gao, Yanghua; Zhao, Xiaoli

doi:10.1016/j.apr.2021.101066

Cited by 28 publications

(2 citation statements)

References 43 publications

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“…They use a random forest and a neural network to combine meteorological factors with urban elements to explore intra-urban PM2.5 concentrations. Lu et al [19] conclude that deviations of hourly ozone prediction by their numerical chemistry transport model can be significantly reduced by machine learning postprocessing. Their postprocessing involves Lasso regression, random forest, and a long short-term memory recurrent neural network.…”

Section: Data-driven Air Pollution Modelingmentioning

confidence: 99%

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Stadtler

Betancourt

Roscher

2022

MAKE

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Air quality is relevant to society because it poses environmental risks to humans and nature. We use explainable machine learning in air quality research by analyzing model predictions in relation to the underlying training data. The data originate from worldwide ozone observations, paired with geospatial data. We use two different architectures: a neural network and a random forest trained on various geospatial data to predict multi-year averages of the air pollutant ozone. To understand how both models function, we explain how they represent the training data and derive their predictions. By focusing on inaccurate predictions and explaining why these predictions fail, we can (i) identify underrepresented samples, (ii) flag unexpected inaccurate predictions, and (iii) point to training samples irrelevant for predictions on the test set. Based on the underrepresented samples, we suggest where to build new measurement stations. We also show which training samples do not substantially contribute to the model performance. This study demonstrates the application of explainable machine learning beyond simply explaining the trained model.

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Section: Data-driven Air Pollution Modelingmentioning

confidence: 99%

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Stadtler

Betancourt

Roscher

2022

MAKE

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“…Another major pollutant is ground-level ozone O 3 , obtained from the combination of two primary pollutants, nitrogen oxides (NO x ) and volatile organic compounds (VOCs). Te 95% of these primary pollutants come from oil, coal, and gasoline combustion in vehicles, industries, power plants, and households, upstream gas and oil production, combustion of residual woods, and the evaporated liquid fuels [8]. Exposure to ozone can signifcantly afect human health, cause asthma, and can lead to premature mortality [9].…”

Section: Introductionmentioning

confidence: 99%

Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

Arora

Singh

et al. 2022

Computational Intelligence and Neuroscience

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In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo’s derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

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A systematic literature review of deep learning neural network for time series air quality forecasting

Zaini

Ean

Ahmed

et al. 2021

Environ Sci Pollut Res

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Adjusting prediction of ozone concentration based on CMAQ model and machine learning methods in Sichuan-Chongqing region, China

Cited by 28 publications

References 43 publications

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

A systematic literature review of deep learning neural network for time series air quality forecasting

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