Intelligent Prediction of Incipient Fault in Vinyl Chloride Production Process based on Deep Learning DOI

Wende Tian,

Hao Wu, Zijian Liu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 472, P. 143474 - 143474

Published: Aug. 27, 2024

Language: Английский

Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models DOI
Gang Li, Zhangkang Shu,

Miaoli Lin

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

Citations

13
Forecasting PPI components using a hybrid hierarchical prediction framework with parameter adaptive transfer algorithm DOI
Jiaming Zhu,

Dai Wan,

Jie Wu

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(5)

Published: Jan. 17, 2025

Language: Английский

Citations

1
Hierarchical prediction of dam deformation based on hybrid temporal network and load-oriented residual correction DOI

En‐Hua Cao,

Tengfei Bao, Rongyao Yuan

et al.

Engineering Structures, Journal Year: 2024, Volume and Issue: 308, P. 117949 - 117949

Published: April 4, 2024

Language: Английский

Citations

7
Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention BILSTM DOI

Zeren Zhou,

Yu Le,

Wang Yu-ming

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 460, P. 142605 - 142605

Published: May 17, 2024

Language: Английский

Citations

5
Coal mine gas emission prediction based on multifactor time series method DOI

Haifei Lin,

Wenjing Li, Shugang Li

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 252, P. 110443 - 110443

Published: Aug. 13, 2024

Language: Английский

Citations

5
Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model DOI
Zhifei Liu, C. Ge, Kang Zheng

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 331, P. 120605 - 120605

Published: May 23, 2024

Language: Английский

Citations

4
Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model DOI
Youness El Mghouchi, Mihaela Tinca Udriștioiu, Hasan Yıldızhan

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 57, P. 102099 - 102099

Published: Aug. 16, 2024

Language: Английский

Citations

4
Hybrid deep learning based prediction for water quality of plain watershed DOI

K. H. Wang,

Lei Liu,

Xuechen Ben

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 262, P. 119911 - 119911

Published: Sept. 2, 2024

Language: Английский

Citations

4
Comprehensive Scale Fusion Networks with High Spatiotemporal Feature Correlation for Air Quality Prediction DOI Creative Commons

Chen-Yi Wu,

Zhengliang Lai,

Yuanrong Xu

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 429 - 429

Published: April 8, 2025

The escalation of industrialization has worsened air quality, underscoring the essential need for accurate forecasting to inform policies and protect public health. Current research primarily emphasized individual spatiotemporal features prediction, neglecting interconnections between these features. To address this, we proposed generative Comprehensive Scale Spatiotemporal Fusion Air Quality Predictor (CSST-AQP). novel dual-branch architecture combines multi-scale spatial correlation analysis with adaptive temporal modeling capture complex interactions in pollutant dispersion enhanced pollution forecasting. Initially, a fusion preprocessing module based on localized high-correlation encodes multidimensional quality indicators geospatial data into unified Then, core employs collaborative framework: processing branch extracts at varying granularities, an enhancement concurrently models local periodicities global evolutionary trends. feature engine hierarchically integrates spatiotemporally relevant regional scales while aggregating from related sites. In experimental results across 14 Chinese regions, CSST-AQP achieves state-of-the-art performance compared LSTM-based networks RMSE 6.11–9.13 μg/m3 R2 0.91–0.93, demonstrating highly robust 60 h capabilities diverse pollutants.

Language: Английский

Citations

0
Improved Prediction of Hourly PM2.5 Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization DOI Creative Commons
Zuhan Liu,

Hong Xian-ping

Toxics, Journal Year: 2025, Volume and Issue: 13(5), P. 327 - 327

Published: April 23, 2025

To address the performance degradation in existing PM2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis integrate ant colony optimization (ACO) algorithm model optimization. Combining meteorological collaborative pollutant data, a (namely stacking-ACO-LSTM model) with much shorter consuming time than that of only long short-term memory (LSTM) networks suitable concentration is established. It can effectively filter out variables higher weights, thereby reducing predictive power model. The hourly trained tested using real-time monitoring data Nanchang City from 2017 to 2019. results show established has high accuracy predicting concentration, compared same without considering space efficiency defective mean square error (MSE) decreases about 99.88%, coefficient determination (R2) increases 2.39%. This study provides new idea cities.

Language: Английский

Citations

0