Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

Hasnain, Ahmad; Sheng, Yehua; Hashmi, Muhammad Zaffar; Bhatti, Uzair Aslam; Ahmed, Zulkifl; Zha, Yong

doi:10.1016/j.chemosphere.2022.137638

Cited by 30 publications

(7 citation statements)

References 49 publications

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“…The two most important parameters of RF are Number of trees and Number of features. Number of decision trees indicates the number of trees in the forest andNumber of features indicates the number of randomly selected features for each decision tree 33 ).…”

Section: Methodsmentioning

confidence: 99%

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Duan¹,

Gong²,

Luo³

et al. 2023

Sci Rep

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Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively.

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Section: Methodsmentioning

confidence: 99%

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Duan¹,

Gong²,

Luo³

et al. 2023

Sci Rep

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show abstract

“…Table 3 shows that the ANN model has strong prediction outcomes and accuracy due to its low RMSE and high R 2 value as compare to other methods of study [44] which used random forest and study [45] which used neural network for prediction without optimization. This model uses adam optimizer function because of providing the highest performance or co-efficient of performance, and MSE and RMSE value among other optimizer functions.…”

Section: The Ann Model's Outcomementioning

confidence: 99%

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Reja,

Amin,

Tasneem

et al. 2023

Atmosphere

Self Cite

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The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.

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“…n_estimators indicates the number of trees in the forest and max_features indicates the number of randomly selected features for each decision tree. [25].…”

Section: Rfmentioning

confidence: 99%

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Duan

Gong

Luo

et al. 2023

Preprint

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The air pollution problem seriously affects economic development and people's health, and an efficient and accurate forecasting model of air quality will help manage air pollution problems. The comparison models chosen by other scholars are often based on derivative models of the proposed model and do not comprehensively compare other types of models with limited accuracy. In this paper, we establishes a combined ARIMA-CNN-LSTM model to predict the air quality index accurately. The model mainly consists of two parts: Using the ARIMA model to fit the linear part of the data and using the CNN-LSTM model to fit the nonlinear part of the data to avoid the problem of blindness in the CNN-LSTM hyperparameter setting. To avoid the dilemma of blindness in a CNN-LSTM hyperparameter setting, this article uses the Dung Beetle Optimizer tool to find the hyperparameters of a CNN-LSTM model, determine the best hyperparameters, and check the accuracy of the model. The proposed model is compared with other widely used models. The results show that the Dung Beetle Optimizer can effectively search for the optimal hyperparameters of the model and can solve the problem of blindness in setting the hyperparameters of the model. And the optimized ARIMA-DBO-CNN-LSTM model has higher predictive accuracy with stronger adaptability in predicting the three cities.

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Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

Cited by 30 publications

References 49 publications

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

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