Multi-source and multivariate ozone prediction based on fuzzy cognitive maps and evidential reasoning theory

http://www.cell.com/article/S2405844022029589/pdf

Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

Abstract Precise and efficient ozone ( $$\hbox {O}_{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> ) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high $$\hbox {O}_{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> pollution levels on human health and ecosystems. However, the complexity of $$\hbox {O}_{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> formation mechanisms in the troposphere presents a significant challenge in modeling $$\hbox {O}_{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects $$\hbox {O}_{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted. 

/pdf/a-comparison-of-machine-learning-methods-for-ozone-pollution-13t7onxd.pdf

A comparison of machine learning methods for ozone pollution prediction

Development of multi-model ensembles using tree-based machine learning methods to assess the future renewable energy potential: case of the East Thrace, Turkey.

The ability to forecast the concentration of air pollutants in an urban region is crucial for decision-makers wishing to reduce the impact of pollution on public health through active measures (e.g. temporary traffic closures). In this study, we present a machine learning approach applied to the forecast of the day-ahead maximum value of the ozone concentration for several geographical locations in southern Switzerland. Due to the low density of measurement stations and to the complex orography of the use case terrain, we adopted feature selection methods instead of explicitly restricting relevant features to a neighbourhood of the prediction sites, as common in spatio-temporal forecasting methods. We then used Shapley values to assess the explainability of the learned models in terms of feature importance and feature interactions in relation to ozone predictions; our analysis suggests that the trained models effectively learned explanatory cross-dependencies among atmospheric variables. Finally, we show how weighting observations helps in increasing the accuracy of the forecasts for specific ranges of ozone's daily peak values.

Dario Marvin

Papers

A data-driven approach to the forecasting of ground-level ozone concentration.

A data-driven approach to forecasting ground-level ozone concentration