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My_Atmos: Novel Method to Analyse Ultrafine Particles Using an Artificial Intelligence Approach

Yahaya, N.Z.; Tight, Miles R.; Tate, James E.; Ibrahim, Zul Fadhli

This presentation will discuss the used of an artificial intelligent method namely the ‘stochastic boosted regression trees’ (BRT) approach that uses an algorithm that applied to an air pollution data namely particle number count concentrations ([PNC]), an ultrafine particles data and particulate matter data case study in United Kingdom and Malaysia. The development of the BRT model involves determining the model algorithm settings of the main model input parameters (learning rate, number of trees and interaction depth) that were tested using the R software (version 3.02) by choosing a10-fold cross-validation approach with combination of lr 0.05 and tc 5 of training set for BRT models. It was found, that the coefficient of determination (R2) value for the BRT best iteration models were above 0.60 for [PNC] in urban environment. The fine and course particle number (FPNC and CPNC) were found to be 0.75 and 0.72 respectively for one of coastal dataset while R2 value of 0.78 and 0.85 were obtained for Malaysia data. Further investigated were performed to rank factor influenced. It was found, that Carbon monoxide (30.28 %) gas and followed by temperature (16.81%) and wind direction (16.4%) were found the high factor influenced PM10 in urban environment. ... mehr

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DOI: 10.5445/IR/1000096830
Veröffentlicht am 25.07.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Aerosolforschung (IMK-AAF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Poster
Publikationsdatum 15.02.2019
Sprache Englisch
Identifikator KITopen-ID: 1000096830
Veranstaltung 7th International Symposium on Ultrafine Particles, Air Quality and Climate (2019), Brüssel, Belgien, 15.05.2019 – 16.05.2019
Schlagwörter Stochastic Boosted Regression Trees (BRT), Algorithm, atmospheric environment data, variable interactions
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