Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

Atmosphere, Год журнала: 2024, Номер 15(12), С. 1432 - 1432

Опубликована: Ноя. 28, 2024

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, data in Dezhou City China are collected from January 2014 to December 2023, multiple deep learning models used forecast PM2.5 concentrations. The ability of the is evaluated compared with observed using various statistical parameters. Although all eight can accomplish forecasting assignments, precision accuracy CNN-GRU-LSTM method 34.28% higher than that ANN method. result shows has best performance other seven models, achieving an R (correlation coefficient) 0.9686 RMSE (root mean square error) 4.6491 μg/m3. values CNN, GRU LSTM 57.00%, 35.98% 32.78% method, respectively. results reveal predictor remarkably improves performances benchmark overall forecasting. This research provides a new perspective for predictive ambient model provide scientific basis prevention control.

Язык: Английский

---

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

Journal of Cleaner Production, Год журнала: 2024, Номер 468, С. 143042 - 143042

Опубликована: Июнь 28, 2024

Язык: Английский

---

The environmental consequences of national Audit governance: An analysis based on county-level data in China

Journal of Environmental Management, Год журнала: 2024, Номер 359, С. 120976 - 120976

Опубликована: Апрель 27, 2024

Язык: Английский

---

An enhanced interval-valued PM2.5 concentration forecasting model with attention-based feature extraction and self-adaptive combination technology

Expert Systems with Applications, Год журнала: 2024, Номер 264, С. 125867 - 125867

Опубликована: Ноя. 29, 2024

Язык: Английский

---

Optimal sizing and cost-benefit assessment of stand-alone microgrids with different energy storage considering dynamic avoided GHG emissions

Journal of Energy Storage, Год журнала: 2024, Номер 109, С. 115128 - 115128

Опубликована: Дек. 27, 2024

Язык: Английский

---

Overview of artificial intelligence methods and data analysis techniques suitable for subsurface datasets

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 1 - 42

Опубликована: Янв. 1, 2025

Язык: Английский

---

Prediction of seasonal variation pollutant sequence based on binomial coupled nonlinear grey Bernoulli model

Grey Systems Theory and Application, Год журнала: 2025, Номер unknown

Опубликована: Апрель 9, 2025

Purpose The traditional grey Bernoulli model often faces limitations when applied to pollutant concentration series, which may exhibit complex seasonal trends and varying data types. To address these challenges, we propose a structural extension of the by integrating binomial equation. This allows for more flexible framework suitable diverse datasets, especially those related environmental pollution. Design/methodology/approach First, time series is decomposed into four relatively stable sub-sequences. Binomial nonlinear models are then integrated predict prediction formula proposed derived directly from definition equation rather than solutions differential equation, thereby minimizing systematic errors. particle swarm optimization algorithm used estimate parameters, while least squares method linear parameters model. Findings BNGBM(1,1) forecast air quality index (AQI), sulfur dioxide (SO 2 ) particulate matter (PM2.5) seven major regions in China. results show that has superior accuracy compared competing models. predicts variations three pollution indicators selected period 2023–2024. concentrations all indices will decrease at different rates. Originality/value well suited sequences exhibiting quasi-exponential growth, whereas polynomial appropriate characterized saturated growth. integration two extends their applicability. In empirical study, despite development China, forecasting demonstrates effective performance indicators.

Язык: Английский

---

Theoretical Study to Support Proposed Framework for Spatial Modeling of PM2.5 Concentration in Pekanbaru City

Опубликована: Сен. 25, 2024

Язык: Английский

---