A novel hybrid fine particulate matter (PM<sub>2.5</sub>) forecasting and its further application system: Case studies in China

Du, Pei; Wang, Jianzhou; Yang, Wendong; Niu, Tong

doi:10.1002/for.2785

Cited by 19 publications

(5 citation statements)

References 50 publications

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“…The research findings indicated that the combination of SO and ELM in a hybrid model performed significantly better than using a single ELM for predicting PM 2.5 concentration. These results align with other scientific papers published in the literature, which have also shown that hybrid models provide improved prediction performance for simulating air pollution parameters 60 – 62 . Additionally, the SO algorithm enhances the prediction accuracy of the standard ELM by determining optimal weights and bias values.…”

Section: Resultssupporting

confidence: 91%

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

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Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

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Section: Resultssupporting

confidence: 91%

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood,

Hameed,

Srivastava

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) has made tremendous progress in recent years in solving numerous engineering in general [27][28][29][30][31][32] and PM 2.5 concentration in particular [33][34][35][36][37][38][39][40][41][42]. ML combines data science, statistics, and computing in an interdisciplinary fashion.…”

Section: Previous Workmentioning

confidence: 99%

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

AlOmar

Khaleel

Al-Saadi

et al. 2022

Advances in Meteorology

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Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.

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“…In another work, the author successfully created a new hybrid model for predicting PM 2.5 levels in outdoors and its use in assessing health consequences and economic losses. According to the authors, the suggested model not only provides early warning information but may also be employed in other systems such as health difficulties [91]. Furthermore, Augustine et al, proposed Ensemble machine learning approaches for predicting and forecasting PM 2.5 concentrations (e.g., XGBoost-RF-ARIMA).…”

Section: Particulate Matter Forecasting Using Ai Techniquesmentioning

confidence: 99%

Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Shankar¹,

Raju²,

Ganesan³

et al. 2022

Sustainability

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Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves.The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

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A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China

Cited by 19 publications

References 50 publications

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

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A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Cited by 19 publications

References 50 publications

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution

Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

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A novel hybrid fine particulate matter (PM_2.5) forecasting and its further application system: Case studies in China