A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting

Zhao, Fang; Li, Weide

doi:10.3390/atmos10040223

Cited by 24 publications

(6 citation statements)

References 52 publications

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“…BP neural network is a widely used feedforward neural network applied in pattern recognition, classification, and prediction [1] . In Wordle prediction, we use BP neural network to predict the number of players and distributed percentage of game guessing attempts.…”

Section: Establishment Of Bp Neural Networkmentioning

confidence: 99%

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Wu,

Li,

Lin

et al. 2023

Frontiers in Artificial Intelligence and Applications

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To further improve the stickiness of puzzle game players, we developed a prediction model based on the BP neural network. Firstly, we preprocessed the existing data to remove some useless information. Then, we introduced the WOA algorithm to optimize the BP neural network model and improve its prediction accuracy. To predict the distribution percentage of attempts to guess a given word in the future, we improved the BP neural network using the multi-input multi-output CIWOA algorithm (Cubic map improvement whale optimization algorithm). The experimental results showed that the WOA-BP prediction model predicted player data for March 1, 2023, to be between 20,000 and 25,000, while the actual data was 19,655. Our prediction model had a small margin of error, indicating good prediction accuracy. Based on this model, we provided reasonable adjustment suggestions for the game company’s later operations.

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Section: Establishment Of Bp Neural Networkmentioning

confidence: 99%

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Wu,

Li,

Lin

et al. 2023

Frontiers in Artificial Intelligence and Applications

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“…The experimental results show that the increase in prediction time does not have a significant impact on the prediction accuracy of the RF model. Zhao et al [21] used an improved support vector machine to predict PM 2.5 concentration. It was verified that the new model had a better generalization ability and performance than other models.…”

Section: Introductionmentioning

confidence: 99%

Application of a Deep Learning Fusion Model in Fine Particulate Matter Concentration Prediction

Zou

Wang³

2023

Atmosphere

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With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

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“…Zhuang et al [30] used it to predict early water shortage; Wang et al constructed a WOA-ELM model for the prediction of carbon emissions in China [52]. Zhao et al constructed a hybrid model based on feature selection and the whale optimization algorithm (WOA) for the prediction of PM2.5 concentrations [53]. Zhao et al proposed a WOA-LSSVM model for the prediction of CO2 emissions [54].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

et al. 2021

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As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

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A Combined Model Based on Feature Selection and WOA for PM2.5 Concentration Forecasting

Cited by 24 publications

References 52 publications

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Predictive Modeling and Analysis of Wordle Player Engagement: Insights and Recommendations for Future Operations

Application of a Deep Learning Fusion Model in Fine Particulate Matter Concentration Prediction

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

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