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 Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City

Toxics, Год журнала: 2025, Номер 13(4), С. 254 - 254

Опубликована: Март 28, 2025

Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Язык: Английский

A CNN-LSTM model for predicting wind speed in non-stationary wind fields in mountainous areas based on wavelet transform and adaptive programming

AIP Advances, Год журнала: 2024, Номер 14(11)

Опубликована: Ноя. 1, 2024

Improving the accuracy of wind speed prediction is crucial for engineering applications and disaster warning due to highly unstable unpredictable nature as an energy source. A model (WT-CNN-LSTM) was constructed based on wavelet decomposition, long short-term memory network (LSTM), convolutional neural (CNN) address non-stationary characteristics in mountainous areas. The sequence decomposed into subsequence columns tested stationarity using adaptive program. data are then reconstructed. established CNN LSTM. final value obtained by overlaying predicted values. results indicated that compared with WT-CNN WT-LSTM models, WT-CNN-LSTM combination proposed this paper reduced MAE, MSE, RMSE indicators 0.10%–0.11%, 0.57%–0.63%, 0.11%–0.13%, respectively. In addition, program eliminates need rely traditional manual empirical values determine parameters, ensuring not affected changes number hidden layer nodes. This information can serve a reference future construction.

Язык: Английский