PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models

Mohamad Japeri, Ahmad Zia Ul-Saufie (2013) PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models. PhD thesis, Universiti Sains Malaysia.

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Abstract

Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 11m, namely PMlO. Statistical modellings are required to predict future PMlO concentrations. The aims of this study are to develop and predict future PMlO concentration for next day (D+ 1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010.

Item Type:	Thesis (PhD)
Subjects:	T Technology > TD Environmental technology. Sanitary engineering > TD878-894 Special types of environment. Including soil pollution, air pollution, noise pollution
Divisions:	Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraan Awam (School of Civil Engineering) > Thesis
Depositing User:	Mr Aizat Asmawi Abdul Rahim
Date Deposited:	13 Apr 2022 09:31
Last Modified:	13 Apr 2022 09:31
URI:	http://eprints.usm.my/id/eprint/52316

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