2026-10-12T13:21:34Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33811999 2026-09-15T17:41:32Z category_146 category_21 category_272 category_734 category_931 category_811 category_133 portal_5 item_type_3 month_year_09_2026

<p>Name Abbreviations.</p> Xiaoliang Zhao (134512) Pinyuan Qiao (24948745) Bandna Bharti (22265299) Hanliang Li (22214002) Shitong Yang (1477990) Qi Shi (159470) Qiuhong Qin (3747496) Jundian Chen (24948748) Space Science Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Plant Biology term memory network pearson correlation analysis feature data fusion convolutional neural network improving prediction accuracy 5 time series final prediction results gspmstl )&# 8212 grid search strategy 5 prediction grid search results demonstrate xianyang cities separately conducted seasonal factors paper proposes multiple seasonal mstl ), model hyperparameters long short heating seasons considering climate <div><p>Accurate prediction of PM2.5 time series remains challenging. To address this issue, this paper proposes a novel PM2.5 concentration prediction model named FATA-GSPMSTL-CNN-LSTM.Firstly,the optimal feature descriptor set is determined by combining Recursive Feature Elimination with Cross-Validation (RFECV) and Pearson correlation analysis. On the basis of Multiple Seasonal-Trend decomposition using Loess (MSTL), a novel adaptive decomposition algorithm—Grid Search-based Power Spectral MSTL (GSPMSTL)—is constructed by introducing spectral density analysis and grid search strategy, which is adopted to decompose the PM2.5 time series, and the dataset is reconstructed through feature data fusion. The newly developed Fata morgana optimization algorithm (FATA) is utilized to optimize the model hyperparameters for further improving prediction accuracy. Finally, the Convolutional Neural Network- Long Short-Term Memory network (CNN-LSTM) is employed to obtain the final prediction results. Considering climate, topography and seasonal factors, PM2.5 prediction and evaluation are separately conducted in heating seasons and non-heating seasons for Guangzhou and Xianyang cities. The results demonstrate that the proposed model achieves higher prediction accuracy and stability, which can provide important application value for air quality early warning and pollution control.</p></div> 2026-09-15T17:41:02Z Dataset Dataset 10.1371/journal.pone.0358271.t001 https://figshare.com/articles/dataset/_p_Name_Abbreviations_p_/33811999 CC BY 4.0