FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4322502464> ?p ?o ?g. }

• W4322502464 endingPage "4077" @default.
• W4322502464 startingPage "4077" @default.
• W4322502464 abstract "Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN–RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN–RF hybrid model has fewer excess residuals at thresholds of 10 μg/m3, 20 μg/m3, and 30 μg/m3. The results revealed that the proposed CNN–RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning." @default.
• W4322502464 created "2023-02-28" @default.
• W4322502464 creator A5020447407 @default.
• W4322502464 creator A5053008131 @default.
• W4322502464 creator A5058184777 @default.
• W4322502464 creator A5060221604 @default.
• W4322502464 date "2023-02-24" @default.
• W4322502464 modified "2023-09-25" @default.
• W4322502464 title "PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework" @default.
• W4322502464 cites W1968840994 @default.
• W4322502464 cites W2091214913 @default.
• W4322502464 cites W2128727570 @default.
• W4322502464 cites W2534610306 @default.
• W4322502464 cites W2556912971 @default.
• W4322502464 cites W2590890587 @default.
• W4322502464 cites W2734945026 @default.
• W4322502464 cites W2737749939 @default.
• W4322502464 cites W2774203468 @default.
• W4322502464 cites W2791942352 @default.
• W4322502464 cites W2804199887 @default.
• W4322502464 cites W2812669263 @default.
• W4322502464 cites W2888192080 @default.
• W4322502464 cites W2898924913 @default.
• W4322502464 cites W2905872298 @default.
• W4322502464 cites W2909340263 @default.
• W4322502464 cites W2912731314 @default.
• W4322502464 cites W2913928348 @default.
• W4322502464 cites W2925301441 @default.
• W4322502464 cites W2954586028 @default.
• W4322502464 cites W2988872163 @default.
• W4322502464 cites W3004417816 @default.
• W4322502464 cites W3005177200 @default.
• W4322502464 cites W3016357108 @default.
• W4322502464 cites W3035717876 @default.
• W4322502464 cites W3085119257 @default.
• W4322502464 cites W3096846826 @default.
• W4322502464 cites W3106794929 @default.
• W4322502464 cites W3108864973 @default.
• W4322502464 cites W3118619916 @default.
• W4322502464 cites W3120971448 @default.
• W4322502464 cites W3132807016 @default.
• W4322502464 cites W3133613421 @default.
• W4322502464 cites W3147988795 @default.
• W4322502464 cites W3156876891 @default.
• W4322502464 cites W3167257048 @default.
• W4322502464 cites W3168781061 @default.
• W4322502464 cites W3175896073 @default.
• W4322502464 cites W3180757470 @default.
• W4322502464 cites W3189604654 @default.
• W4322502464 cites W3195320381 @default.
• W4322502464 cites W3198547088 @default.
• W4322502464 cites W3210647140 @default.
• W4322502464 cites W4206177074 @default.
• W4322502464 cites W4206665491 @default.
• W4322502464 cites W4220910788 @default.
• W4322502464 cites W4282007141 @default.
• W4322502464 cites W4293106679 @default.
• W4322502464 cites W4294203719 @default.
• W4322502464 cites W4294872360 @default.
• W4322502464 cites W4304080892 @default.
• W4322502464 cites W4308961914 @default.
• W4322502464 cites W4311662183 @default.
• W4322502464 cites W4319600113 @default.
• W4322502464 cites W590735017 @default.
• W4322502464 doi "https://doi.org/10.3390/ijerph20054077" @default.
• W4322502464 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36901088" @default.
• W4322502464 hasPublicationYear "2023" @default.
• W4322502464 type Work @default.
• W4322502464 citedByCount "2" @default.
• W4322502464 countsByYear W43225024642023 @default.
• W4322502464 crossrefType "journal-article" @default.
• W4322502464 hasAuthorship W4322502464A5020447407 @default.
• W4322502464 hasAuthorship W4322502464A5053008131 @default.
• W4322502464 hasAuthorship W4322502464A5058184777 @default.
• W4322502464 hasAuthorship W4322502464A5060221604 @default.
• W4322502464 hasBestOaLocation W43225024641 @default.
• W4322502464 hasConcept C105795698 @default.
• W4322502464 hasConcept C108583219 @default.
• W4322502464 hasConcept C119857082 @default.
• W4322502464 hasConcept C119898033 @default.
• W4322502464 hasConcept C124101348 @default.
• W4322502464 hasConcept C138885662 @default.
• W4322502464 hasConcept C139945424 @default.
• W4322502464 hasConcept C153180895 @default.
• W4322502464 hasConcept C154945302 @default.
• W4322502464 hasConcept C169258074 @default.
• W4322502464 hasConcept C26517878 @default.
• W4322502464 hasConcept C2776401178 @default.
• W4322502464 hasConcept C33923547 @default.
• W4322502464 hasConcept C38652104 @default.
• W4322502464 hasConcept C41008148 @default.
• W4322502464 hasConcept C41895202 @default.
• W4322502464 hasConcept C45942800 @default.
• W4322502464 hasConcept C81363708 @default.
• W4322502464 hasConcept C83546350 @default.
• W4322502464 hasConceptScore W4322502464C105795698 @default.
• W4322502464 hasConceptScore W4322502464C108583219 @default.
• W4322502464 hasConceptScore W4322502464C119857082 @default.

• next