FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386445327> ?p ?o ?g. }

• W4386445327 endingPage "101916" @default.
• W4386445327 startingPage "101916" @default.
• W4386445327 abstract "With the development and improvement of optimization algorithms, machine learning models have become more important in air quality prediction. These models can be used to address problems associated with atmospheric environmental pollutants like ozone and facilitate the development of appropriate control policies. Firstly, the data sets of meteorological and pollution in Nanjing and four nearby cities from 2018 to 2021 were divided into four sections to build mixed models. The extreme gradient boosting (XGBoost) algorithm was improved by the permutation importance method (PIM) and Pearson correlation coefficient to acquire the most important features of the seasonal model and analysis of the effect on ozone prediction. The results reflect that the NO2, PM2.5, and PM10 parameters have an important influence, accounting for an average of 23% of prediction, and the average influence of the meteorological parameters of the solar radiation exceeds 11%. Secondly, the particle swarm optimization (PSO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) algorithms were used to optimize the SVR model parameters to predict hourly ozone concentration in January, April, July, and September 2022. The R2 values are 0.95, 0.92, 0.85, and 0.84, respectively, and the RMSE values are 5.2, 12.4, 18.0 and 16.6 μg/m3, respectively. Finally, the impact of hourly ozone concentration on population health was studied by a risk assessment of the population density-weighted pollution exposure. The result shows that the influence on humans gradually decreased yearly from 2018 to 2021, the average variation from 2.6% to −1.1%, and health risks during the evening rush hours are still prevalent and require further attention." @default.
• W4386445327 created "2023-09-06" @default.
• W4386445327 creator A5013592125 @default.
• W4386445327 creator A5014687439 @default.
• W4386445327 creator A5068252474 @default.
• W4386445327 creator A5092757004 @default.
• W4386445327 date "2023-11-01" @default.
• W4386445327 modified "2023-10-16" @default.
• W4386445327 title "Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment" @default.
• W4386445327 cites W1968599670 @default.
• W4386445327 cites W2000656928 @default.
• W4386445327 cites W2001979953 @default.
• W4386445327 cites W2060750669 @default.
• W4386445327 cites W2061438946 @default.
• W4386445327 cites W2102636708 @default.
• W4386445327 cites W2127603271 @default.
• W4386445327 cites W2172064003 @default.
• W4386445327 cites W2189777652 @default.
• W4386445327 cites W2412056931 @default.
• W4386445327 cites W2602881558 @default.
• W4386445327 cites W2750361126 @default.
• W4386445327 cites W2767085346 @default.
• W4386445327 cites W2770073247 @default.
• W4386445327 cites W2884300009 @default.
• W4386445327 cites W2913711732 @default.
• W4386445327 cites W2943372506 @default.
• W4386445327 cites W2954451295 @default.
• W4386445327 cites W2954824423 @default.
• W4386445327 cites W2971198853 @default.
• W4386445327 cites W2981470846 @default.
• W4386445327 cites W2998480833 @default.
• W4386445327 cites W3009252385 @default.
• W4386445327 cites W3014186522 @default.
• W4386445327 cites W3018215981 @default.
• W4386445327 cites W3025103497 @default.
• W4386445327 cites W3034075062 @default.
• W4386445327 cites W3081479954 @default.
• W4386445327 cites W3082237164 @default.
• W4386445327 cites W3083472790 @default.
• W4386445327 cites W3091850311 @default.
• W4386445327 cites W3103621885 @default.
• W4386445327 cites W3126231171 @default.
• W4386445327 cites W3131798652 @default.
• W4386445327 cites W3132912074 @default.
• W4386445327 cites W3147233169 @default.
• W4386445327 cites W3194162282 @default.
• W4386445327 cites W3197819402 @default.
• W4386445327 cites W3215556695 @default.
• W4386445327 cites W4200586036 @default.
• W4386445327 cites W4223488490 @default.
• W4386445327 cites W4226277510 @default.
• W4386445327 cites W4283698843 @default.
• W4386445327 cites W4295038101 @default.
• W4386445327 cites W4312140875 @default.
• W4386445327 doi "https://doi.org/10.1016/j.apr.2023.101916" @default.
• W4386445327 hasPublicationYear "2023" @default.
• W4386445327 type Work @default.
• W4386445327 citedByCount "0" @default.
• W4386445327 crossrefType "journal-article" @default.
• W4386445327 hasAuthorship W4386445327A5013592125 @default.
• W4386445327 hasAuthorship W4386445327A5014687439 @default.
• W4386445327 hasAuthorship W4386445327A5068252474 @default.
• W4386445327 hasAuthorship W4386445327A5092757004 @default.
• W4386445327 hasConcept C105795698 @default.
• W4386445327 hasConcept C119857082 @default.
• W4386445327 hasConcept C126314574 @default.
• W4386445327 hasConcept C127313418 @default.
• W4386445327 hasConcept C139945424 @default.
• W4386445327 hasConcept C153294291 @default.
• W4386445327 hasConcept C169258074 @default.
• W4386445327 hasConcept C178790620 @default.
• W4386445327 hasConcept C185592680 @default.
• W4386445327 hasConcept C205649164 @default.
• W4386445327 hasConcept C2780092901 @default.
• W4386445327 hasConcept C2908647359 @default.
• W4386445327 hasConcept C33923547 @default.
• W4386445327 hasConcept C39432304 @default.
• W4386445327 hasConcept C41008148 @default.
• W4386445327 hasConcept C508106653 @default.
• W4386445327 hasConcept C559116025 @default.
• W4386445327 hasConcept C70153297 @default.
• W4386445327 hasConcept C71924100 @default.
• W4386445327 hasConcept C85617194 @default.
• W4386445327 hasConcept C91586092 @default.
• W4386445327 hasConcept C99454951 @default.
• W4386445327 hasConceptScore W4386445327C105795698 @default.
• W4386445327 hasConceptScore W4386445327C119857082 @default.
• W4386445327 hasConceptScore W4386445327C126314574 @default.
• W4386445327 hasConceptScore W4386445327C127313418 @default.
• W4386445327 hasConceptScore W4386445327C139945424 @default.
• W4386445327 hasConceptScore W4386445327C153294291 @default.
• W4386445327 hasConceptScore W4386445327C169258074 @default.
• W4386445327 hasConceptScore W4386445327C178790620 @default.
• W4386445327 hasConceptScore W4386445327C185592680 @default.
• W4386445327 hasConceptScore W4386445327C205649164 @default.
• W4386445327 hasConceptScore W4386445327C2780092901 @default.
• W4386445327 hasConceptScore W4386445327C2908647359 @default.
• W4386445327 hasConceptScore W4386445327C33923547 @default.

• next