An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Jiang, Wenxin; Zhu, Guo-Chang; Shen, Yi-Yun; Xie, Qian; Ji, Min; Yu, Yongtao

doi:10.3390/e24121803

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Composite air quality prediction frameworks, integrating various machine learning and deep learning models, have shown even more promising outcomes. For example, combining long short-term memory networks with Gated Recurrent Unit model [27], random subspace [28], attention mechanism [29], XGBoosting tree [30], random forest regression based on linear regression [31], and an adaptive fuzzy inference system with the Extreme Learning Machine [32] has yielded substantial advancements. Phruksahiran, N [33] introduced the Geographically Weighted Prediction Method (GWP), leveraging optimal machine learning algorithms and additional prediction variables.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

Chang,

Chen,

et al. 2023

Sustainability

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Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R2 values of PM2.5, PM10, SO2, CO, NO2, and O3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation.

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Section: Introductionmentioning

confidence: 99%

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

Chang,

Chen,

et al. 2023

Sustainability

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“…The RF + BP + GA model has the best fitting effect among the air quality-meteorology correlation models, basically achieves complete fitting, and can accurately predict the AQI value of air quality. Jiang et al [45] established the fusion model of the limit gradient lifting algorithm + BP + autoregressive moving average model to jointly predict the air quality in Changping District, Beijing. They found that the prediction effect of the proposed model was more accurate than the air quality prediction of a single limit gradient lifting algorithm, BPNN, or autoregressive moving average model.…”

Section: Discussionmentioning

confidence: 99%

Air Quality—Meteorology Correlation Modeling Using Random Forest and Neural Network

et al. 2023

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Under the global warming trend, the diffusion of air pollutants has intensified, causing extremely serious environmental problems. In order to improve the air quality–meteorology correlation model’s prediction accuracy, this work focuses on the management strategy of the environmental ecosystem under the Artificial Intelligence (AI) algorithm and explores the correlation between air quality and meteorology. Xi’an city is selected as an example. Then, the theoretical knowledge is explained for Random Forest (RF), Backpropagation Neural Network (BPNN), and Genetic Algorithm (GA) in AI. Finally, GA is used to optimize and predict the weights and thresholds of the BPNN. Further, a fusion model of RF + BP + GA is proposed to predict the air quality and meteorology correlation. The proposed air quality–meteorology correlation model is applied to forest ecosystem management. Experimental analysis reveals that average temperature positively correlates with Air Quality Index (AQI), while relative humidity and wind speed negatively correlate with AQI. Moreover, the proposed RF + BP + GA model’s prediction error for AQI is not more than 0.32, showing an excellently fitting effect with the actual value. The air-quality prediction effect of the meteorological correlation model using RF is slightly lower than the real measured value. The prediction effect of the BP–GA model is slightly higher than the real measured value. The prediction effect of the air quality–meteorology correlation model combining RF and BP–GA is the closest to the real measured value. It shows that the air quality–meteorology correlation model using the fusion model of RF and BP–GA can predict AQI with the utmost accuracy. This work provides a research reference regarding the AQI value of the correlation model of air quality and meteorology and provides data support for the analysis of air quality problems.

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“…Time series generally refers to a set of random variables derived from the observation of the development and change process of something and collected at a certain frequency, with characteristics of time dependency, seasonality, trend, and randomness. Time series forecasting is vital in many real-world scenarios, such as traffic forecasts [ 1 , 2 ], air quality prediction [ 3 , 4 , 5 ], and water quality monitoring [ 6 ]. Especially in the field of healthcare, the forecasting of future incidence and mortality rates among patients enables effective control and prevention of diseases [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Time Series Forecasting Based on Graph Neural Networks

Han,

Huang,

Pan

et al. 2023

Entropy

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Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.

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An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Cited by 7 publications

References 35 publications

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods

Air Quality—Meteorology Correlation Modeling Using Random Forest and Neural Network

Multi-Task Time Series Forecasting Based on Graph Neural Networks

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