A hybrid VMD-LSTM/GRU model to predict non-stationary and irregular waves on the east coast of China

Ocean Engineering, Journal Year: 2023, Volume and Issue: 276, P. 114136 - 114136

Published: March 28, 2023

Language: Английский

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

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 26, 2023

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient accurate air quality prediction model would help to manage the problem. In this paper, we build combined accurately predict AQI based on real data from four cities. First, use ARIMA fit linear part of CNN-LSTM non-linear avoid blinding in hyperparameter setting. Then, dilemma setting, Dung Beetle Optimizer algorithm find hyperparameters model, determine optimal hyperparameters, check accuracy model. Finally, compare proposed with nine other widely used models. The experimental results show paper outperforms comparison models terms root mean square error (RMSE), absolute (MAE) coefficient determination (R 2 ). RMSE values for cities were 7.594, 14.94, 7.841 5.496; MAE 5.285, 10.839, 5.12 3.77; R 0.989, 0.962, 0.953 respectively.

Language: Английский

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A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 468, P. 143042 - 143042

Published: June 28, 2024

Language: Английский

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Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction

Machine Learning with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 100624 - 100624

Published: Jan. 1, 2025

Language: Английский

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Modeling PM2.5 forecast using a self-weighted ensemble GRU network: Method optimization and evaluation

Ecological Indicators, Journal Year: 2023, Volume and Issue: 156, P. 111138 - 111138

Published: Nov. 6, 2023

Due to the rapid industrial development and global concern about air pollution, understanding dynamics of PM2.5 concentration has become a key aspect quality prediction. Many deep learning mode decomposition techniques have been explored capture temporal nonlinear features data. However, most existing methods ignore differences in prediction losses individual subsequences, resulting lower accuracy. To address this limitation, we proposed an ensemble gated recurrent unit (GRU) model that incorporated self-weighted total loss function based on variational (VMD). In approach, series were decomposed using VMD, then each subsequence (including residual sequence) was fed into GRU predicted calculated. For output optimal predictions, used adaptively optimize for subsequence. Specifically, larger weights assigned model's subsequences with higher predictive better focus those losses. addition, hyperparameter adjusted adapt various datasets different domains. Experimental results three show our performs than VMD-GRU single models. This validates effectiveness model. Our approach advantage plug-and-play, making it easier seamlessly integrate pattern

Language: Английский

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An Integrated Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Optimize LSTM for Significant Wave Height Forecasting

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(2), P. 435 - 435

Published: Feb. 16, 2023

In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters energy, significant height (SWH) is difficult to accurately predict due complex ocean conditions ubiquitous chaotic phenomena in nature. Therefore, this paper proposes an integrated CEEMDAN-LSTM joint model. Traditional computational fluid dynamics (CFD) a long calculation period high capital consumption, but artificial intelligence methods have advantage accuracy fast convergence. CEEMDAN commonly used method digital signal processing mechanical engineering, not yet been SWH prediction. It better performance than EMD EEMD more suitable LSTM addition, also novel filter formulation outliers based on improved violin-box plot. The final empirical results show that significantly outperforms each forecast duration, improving prediction accuracy. particular, duration 1 h, improvement over LSTM, with 71.91% RMSE, 68.46% MAE 6.80% NSE, respectively. summary, our model can improve real-time scheduling capability marine engineering maintenance operations.

Language: Английский

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Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 11, 2024

Abstract Air pollution poses a significant threat to the health of environment and human well-being. The air quality index (AQI) is an important measure that describes degree its impact on health. Therefore, accurate reliable prediction AQI critical but challenging due non-linearity stochastic nature particles. This research aims propose hybrid deep learning model based Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) XGBoost modelling techniques. Daily data were collected from official Seoul registry for period 2021 2022. first preprocessed through ARIMA capture fit linear part followed by architecture developed in pretraining–finetuning framework non-linear data. used convolution extract features original data, then QPSO optimize hyperparameter LSTM network mining long-terms time series features, was adopted fine-tune final model. robustness reliability resulting assessed compared with other widely models across meteorological stations. Our proposed achieves up 31.13% reduction MSE, 19.03% MAE 2% improvement R-squared best appropriate conventional model, indicating much stronger magnitude relationships between predicted actual values. overall results show attentive inspired more feasible efficient predicting at both city-wide station-specific levels.

Language: Английский

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A nonlinear grey model with seasonal weighted fractional accumulation for triangular fuzzy number series and its application to forecast PM2.5

Grey Systems Theory and Application, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Purpose Accurate prediction of PM2.5 concentration is essential for the government to formulate and implement effective environmental policies management measures improve air quality. series exhibits seasonal, nonlinear, uncertain characteristics. A seasonal weighted fractional nonlinear grey model triangular fuzzy number established based on Bernoulli by introducing accumulation generating operator. Design/methodology/approach First, actual sequence processed using a new operator weaken its seasonality. The sine function time power are introduced into perform processing again, thereby enhancing model’s adaptability series. Secondly, parameters transformed matrix form so as directly Additionally, optimal algorithm selected through comparison experiments used determine parameters. Findings Five models predict concentrations in Shanghai, China San Francisco, United States America (USA). findings show that with operator, can better simulate characteristics compared other models. Then, next four quarters two cities predicted analyzed. Originality/value dynamic volatility. When represented series, it reflects complexity uncertainty data, which helps people make more accurate decisions. capacity precisely forecast improved large part this work.

Language: Английский

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Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA Model) in Hunan Province, China

Sustainability, Journal Year: 2024, Volume and Issue: 16(19), P. 8471 - 8471

Published: Sept. 29, 2024

Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, autoregressive integrated moving average model (ARIMA) is introduced evaluate and predict current status quality China, constructed ARIMA has an excellent prediction effect Province. The following conclusions are obtained through analysis based model: (1) shows a year-on-year improvement trend; (2) method reliable effective can accurately analyze concentrations air pollutants (PM2.5, PM10, SO2, CO) quality, results show that outdoor will improve gradually each year 2024 2028; (3) this study contributes better understanding ambient during 2016–2023 provides good forecasting for period 2024–2028.

Language: Английский

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Modeling PM2.5 urbane pollution using hybrid models incorporating decomposition and multiple factors

Urban Climate, Journal Year: 2025, Volume and Issue: 60, P. 102338 - 102338

Published: Feb. 15, 2025

Language: Английский

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