The Development and Application of Machine Learning in Atmospheric Environment Studies

Zheng, Lianming; Lin, Rui; Wang, Xuemei

doi:10.3390/rs13234839

Cited by 21 publications

(4 citation statements)

References 132 publications

(133 reference statements)

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“…In comparison, the values predicted by MLR tended to be more conservative. Given the promising results from RF in the present study, it could be recommended to investigate the applicability of other machine learning techniques to overcome the abovementioned limitation of RF (e.g., XGBoost, artificial neural networks, ARIMA) to determine whether they will exhibit similar trends [18,21,54,55].…”

Section: Comparison Between Prediction Methodsmentioning

confidence: 99%

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

et al. 2022

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Despite Central and Northern Asia having several cities sharing a similar harsh climate and grave air quality concerns, studies on air pollution modeling in these regions are limited. For the first time, the present study uses multiple linear regression (MLR) and a random forest (RF) algorithm to predict PM2.5 concentrations in Astana, Kazakhstan during heating and non-heating periods (predictive variables: air pollutant concentrations, meteorological parameters). Estimated PM2.5 was then used for Disability-Adjusted Life Years (DALY) risk assessment. The RF model showed higher accuracy than the MLR model (R2 from 0.79 to 0.98 in RF). MLR yielded more conservative predictions, making it more suitable for use with a lower number of predictor variables. PM10 and carbon monoxide concentrations contributed most to the PM2.5 prediction (both models), whereas meteorological parameters showed lower association. Estimated DALY for Astana’s population (2019) ranged from 2160 to 7531 years. The developed methodology is applicable to locations with comparable air pollution and climate characteristics. Its output would be helpful to policymakers and health professionals in developing effective air pollution mitigation strategies aiming to mitigate human exposure to ambient air pollutants.

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Section: Comparison Between Prediction Methodsmentioning

confidence: 99%

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

et al. 2022

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“…Machine learning has a great application potential in the field of earth environment systems [121]. In particular, DL can extract the spatio-temporal structure and characteristics of data, and it is a good solution to the problem of strong time dependence such as rainfall simulation and prediction.…”

Section: Extending Machine Learning Capabilitiesmentioning

confidence: 99%

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks

Lian

Wei

Sun

et al. 2022

Sensors

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As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

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“…Many different machine-learning models exist, and have been used widely in environmental studies (Zheng et al, 2021). Two machine-learning tools (rmweather and Prophet) were chosen for this work, and were used to derive statistical relationships between atmospheric CH 4 concentrations and complementary input parameters as a function of time.…”

Section: Machine Learning Toolsmentioning

confidence: 99%

A case study application of machine-learning for the detection of greenhouse gas emission sources

Shaw¹,

Allen²,

Topping³

et al. 2022

Atmospheric Pollution Research

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The Development and Application of Machine Learning in Atmospheric Environment Studies

Cited by 21 publications

References 132 publications

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks

A case study application of machine-learning for the detection of greenhouse gas emission sources

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