Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

QR Code

Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia

This research aims to forecast the daily average PM10 concentrations in Peninsular Malaysia by using univariate modelling, i.e. time series modelling and regression modelling. In time series analysis, a typical problem in forecasting is the underestimation of the peaks. Since the series of PM10 conc...

Full description

Bibliographic Details
Main Author:	Ng, Kar Yong
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/47826/ http://eprints.usm.my/47826/1/STATISTICAL%20MODELLING%20FOR.pdf

Similar Items

Probabilistic And Distribution Modelling For Predicting Pm10 Concentration In Malaysia
by: Hamid, Hazrul Abdul
Published: (2013)

Statistical modeling of extreme rainfall in peninsular Malaysia
by: Liew, Woon Shean
Published: (2020)

Imputing missing values in modelling the PM10 concentrations
by: Nuradhiathy Abd Razak,, et al.
Published: (2014)

Modeling And Prediction Of Pm10 Concentration During High Particulate Events In Malaysia
by: Yusof, Noor Faizah Fitri Bt Md
Published: (2009)

Determining of PM 10 aqi over Malaysia using NOAA-14 AVHRR satellite data
by: Ahmad, Asmala, et al.
Published: (2005)

Fitting the statistical distributions to the daily rainfall amount in Peninsular Malaysia
by: Jamaludin, Suhaila, et al.
Published: (2007)

Bayesian Forecasting Model For Inflation Data
by: Zul Amry, Zul Amry
Published: (2015)

Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
by: Ramli, Norazrin
Published: (2021)

Bayesian extreme for modeling high PM10 concentration in Johor
by: Mohd Amin, Nor Azrita, et al.
Published: (2015)

Fitting the statistical distribution for daily rainfall in Peninsular Malaysia based on AIC criterion
by: Syed Jamaludin, Shariffah Suhaila, et al.
Published: (2008)

Modeling Locational Differences And Prediction Of Temporal Concentration Of Pm10 Using Time Series Analysis
by: Sansuddin, Nurulilyana
Published: (2010)

New tourism product forecasting - application of Bass Diffusion Model and Grey Forecasting Model
by: N., Abu, et al.
Published: (2020)

New tourism product forecasting – application of bass diffusion model and grey forecasting model
by: Abu, Noratikah, et al.
Published: (2020)

Diurval Variation Of PM10 And Carbon Monoxide Concentrations In Penang
by: Abd. Manaff, Liana Syafinaz
Published: (2005)

Model Building And Forecasting Of Climate Data For Tourism Area In Bangladesh
by: Hossen, Sayed Mohibul
Published: (2022)

Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
by: Japeri, Ahmad Zia Ul-Saufie Mohamad
Published: (2013)

Future Daily PM10 Concentrations Prediction In Malaysia Using Artificial Neural Networks, Stochastic Regression And Hybrid Models
by: Yahaya, Ahmad Shukri, et al.
Published: (2015)

Prediction Of PM10 Concentration Using Multiple Linear Regression And Bayesian Model Averaging
by: Ismail, Hafizahizzati
Published: (2017)

Prediction of pm10 concentration using multiple linear regression and support vector machine
by: Zailan, Masezatti
Published: (2018)

Extreme value theory for modeling and prediction of high PM10 concentration in Johor
by: Mohd Amin, Nor Azrita, et al.
Published: (2013)

ARIMA and symmetric GARCH-type models in forecasting Malaysia gold price
by: Siti Roslindar, Yaziz, et al.
Published: (2019)

Electricity demand forecasting in Malaysia using seasonal Box-Jenkins model
by: Syarranur, Zaim, et al.
Published: (2025)

Bivariate extreme value with application to PM10 concentration analysis
by: Mohd Amin, Nor Azrita, et al.
Published: (2014)

An investigation of PM10 concentrations at one site of Kuala Lumpur
by: Mohd. Yusoff, Mohd. Rashid
Published: (1987)

New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models
by: Hussain, Jassim Nassir
Published: (2013)

Statistical approach on grading: mixture modeling
by: Md. Desa, Zairul Nor Deana
Published: (2006)

Prediction Of Pm10 Concentrations Using Extreme Value Distributions (Evd) Classical And Bayesian Approaches
by: Ahmat, Hasfazilah
Published: (2016)

Time series forecasting for tourism industry in Malaysia
by: Noratikah, Abu, et al.
Published: (2024)

STATISTICS WITH SPSS FOR RESEARCH
by: Tay, Choo Chuan, et al.
Published: (2011)

Forecasting Stock Market Volatility Using Wavelet Transformation Algorithm Of Garch Model
by: Audu, Buba
Published: (2017)

MAT363 – Statistical Inference
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2022)

MST561 – Statistical Inference
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2022)

MST564 – Statistical Reliability
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2022)

MAT161 - Elementary Statistics
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2024)

MAT363 - Statistical Inference
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2024)

MAT161 – Elementary Statistics
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2022)

MAT 161 - Elementary Statistics
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2022)

MAT161 - Elementary Statistics
by: PPSM, Pusat Pengajian Sains Matematik
Published: (2024)

Investigation into the effect of atmospheric particulate matter (PM2.5 and PM10) concentrations on GPS signals
by: Lau, Lawrence, et al.
Published: (2017)

Statistical model and prediction of pineapple plant weight
by: Usman, Mustofa, et al.
Published: (2015)