FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381678571> ?p ?o ?g. }

• W4381678571 abstract "Abstract Precise and efficient ozone ( $$hbox {O}_{3}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> ) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high $$hbox {O}_{3}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> pollution levels on human health and ecosystems. However, the complexity of $$hbox {O}_{3}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> formation mechanisms in the troposphere presents a significant challenge in modeling $$hbox {O}_{3}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects $$hbox {O}_{3}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mtext>O</mml:mtext> <mml:mn>3</mml:mn> </mml:msub> </mml:math> concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted." @default.
• W4381678571 created "2023-06-23" @default.
• W4381678571 creator A5071141077 @default.
• W4381678571 creator A5087572406 @default.
• W4381678571 creator A5090118631 @default.
• W4381678571 date "2023-05-15" @default.
• W4381678571 modified "2023-09-30" @default.
• W4381678571 title "A comparison of machine learning methods for ozone pollution prediction" @default.
• W4381678571 cites W1964357740 @default.
• W4381678571 cites W1965421670 @default.
• W4381678571 cites W1970436524 @default.
• W4381678571 cites W1985817801 @default.
• W4381678571 cites W1991041654 @default.
• W4381678571 cites W1998517707 @default.
• W4381678571 cites W2010043809 @default.
• W4381678571 cites W2061078639 @default.
• W4381678571 cites W2067857935 @default.
• W4381678571 cites W2100036870 @default.
• W4381678571 cites W2109316362 @default.
• W4381678571 cites W2113418007 @default.
• W4381678571 cites W2155861891 @default.
• W4381678571 cites W2169656816 @default.
• W4381678571 cites W2220787965 @default.
• W4381678571 cites W2263682090 @default.
• W4381678571 cites W2506183471 @default.
• W4381678571 cites W2507265313 @default.
• W4381678571 cites W2523463307 @default.
• W4381678571 cites W2579196193 @default.
• W4381678571 cites W2790149694 @default.
• W4381678571 cites W2811192320 @default.
• W4381678571 cites W2889897869 @default.
• W4381678571 cites W2919115771 @default.
• W4381678571 cites W2932881901 @default.
• W4381678571 cites W2939320242 @default.
• W4381678571 cites W2942485354 @default.
• W4381678571 cites W2946890823 @default.
• W4381678571 cites W2955624416 @default.
• W4381678571 cites W2965411974 @default.
• W4381678571 cites W2981938452 @default.
• W4381678571 cites W3009252385 @default.
• W4381678571 cites W3011230323 @default.
• W4381678571 cites W3024420713 @default.
• W4381678571 cites W3034555743 @default.
• W4381678571 cites W3037226097 @default.
• W4381678571 cites W3040543281 @default.
• W4381678571 cites W3045064697 @default.
• W4381678571 cites W3049512327 @default.
• W4381678571 cites W3080816325 @default.
• W4381678571 cites W3088970435 @default.
• W4381678571 cites W3092565460 @default.
• W4381678571 cites W3114452526 @default.
• W4381678571 cites W3115700835 @default.
• W4381678571 cites W3132912074 @default.
• W4381678571 cites W3134561016 @default.
• W4381678571 cites W3134768566 @default.
• W4381678571 cites W3164087818 @default.
• W4381678571 cites W3177398207 @default.
• W4381678571 cites W3196658306 @default.
• W4381678571 cites W3197819402 @default.
• W4381678571 cites W3213119758 @default.
• W4381678571 cites W4200474599 @default.
• W4381678571 cites W4224930339 @default.
• W4381678571 cites W4225405154 @default.
• W4381678571 cites W4254159526 @default.
• W4381678571 cites W4292671038 @default.
• W4381678571 cites W4303104919 @default.
• W4381678571 cites W4312661908 @default.
• W4381678571 doi "https://doi.org/10.1186/s40537-023-00748-x" @default.
• W4381678571 hasPublicationYear "2023" @default.
• W4381678571 type Work @default.
• W4381678571 citedByCount "2" @default.
• W4381678571 countsByYear W43816785712023 @default.
• W4381678571 crossrefType "journal-article" @default.
• W4381678571 hasAuthorship W4381678571A5071141077 @default.
• W4381678571 hasAuthorship W4381678571A5087572406 @default.
• W4381678571 hasAuthorship W4381678571A5090118631 @default.
• W4381678571 hasBestOaLocation W43816785711 @default.
• W4381678571 hasConcept C11413529 @default.
• W4381678571 hasConcept C119857082 @default.
• W4381678571 hasConcept C154945302 @default.
• W4381678571 hasConcept C41008148 @default.
• W4381678571 hasConceptScore W4381678571C11413529 @default.
• W4381678571 hasConceptScore W4381678571C119857082 @default.
• W4381678571 hasConceptScore W4381678571C154945302 @default.
• W4381678571 hasConceptScore W4381678571C41008148 @default.
• W4381678571 hasFunder F4320322320 @default.
• W4381678571 hasIssue "1" @default.
• W4381678571 hasLocation W43816785711 @default.
• W4381678571 hasOpenAccess W4381678571 @default.
• W4381678571 hasPrimaryLocation W43816785711 @default.
• W4381678571 hasRelatedWork W2961085424 @default.
• W4381678571 hasRelatedWork W3046775127 @default.
• W4381678571 hasRelatedWork W3170094116 @default.
• W4381678571 hasRelatedWork W4205958290 @default.
• W4381678571 hasRelatedWork W4225307033 @default.
• W4381678571 hasRelatedWork W4285260836 @default.
• W4381678571 hasRelatedWork W4286629047 @default.
• W4381678571 hasRelatedWork W4306321456 @default.
• W4381678571 hasRelatedWork W4306674287 @default.
• W4381678571 hasRelatedWork W4224009465 @default.

• next