Future Daily PM10 Concentrations
Prediction In Malaysia Using Artificial
Neural Networks, Stochastic Regression
And Hybrid Models

QR Code

Future Daily PM10 Concentrations Prediction In Malaysia Using Artificial Neural Networks, Stochastic Regression And Hybrid Models

Bibliographic Details
Main Authors:	Yahaya, Ahmad Shukri, Ramli, Nor Azam, Md Yusof, Noor Faizah Fitri
Format:	Monograph
Published:	Universiti Sains Malaysia 2015
Subjects:	TA1-2040 Engineering (General). Civil engineering (General)
Online Access:	http://eprints.usm.my/37861/

Similar Items

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

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

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

PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
by: Mohamad Japeri, Ahmad Zia Ul-Saufie
Published: (2013)

Prediction Of PM10 Concentration Using Multiple Linear Regression And Support Vector Machine
by: Zailan, Masezatti
Published: (2018)

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

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

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

Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees
by: Hamid, Nur Haziqah Mohd
Published: (2017)

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

Imputation methods on daily PM10 data (2010-15)
by: Abd Rani, Nurul Latiffah, et al.
Published: (2019)

Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique
by: Yahaya, Noor Zaitun, et al.
Published: (2017)

Determination Of Coefficient For Pm10 Monitoring Using An Optical Direct Reading Instrument And Beta Attenuation Monitor
by: Mohtar, Zul Azmi
Published: (2013)

PM10 analysis for three industrialized areas using extreme value
by: Hasfazilah Ahmat,, et al.
Published: (2015)

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

Modeling The Fiuctuational And Transformation Characteristics Of Peak Ground Level Ozone in Malaysian Ambient Air
by: Ramli , Nor Azam, et al.
Published: (2016)

Artificial Neural Network Model Prediction of Glucose by Enzymatic Hydrolysis of Rice Straw
by: Jaya, Erniza Mohd Johan, et al.
Published: (2014)

Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
by: Dayang Suhaila, Awang Suhaili
Published: (2006)

Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks
by: Papzan, Ali
Published: (2011)

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

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

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

Statistical Modelling For Forecasting Pm10 Concentrations In Peninsular Malaysia
by: Ng, Kar Yong
Published: (2017)

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

Time series analysis of PM10 concentration in Parit Raja residential area
by: Raffee, Ahmad Fauzi, et al.
Published: (2018)

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

Classification for ammonia in water by specific concentration using artificial neural network (ANN)
by: Muhamad Faruqi, Zahari, et al.

A Framework For Monitoring And Modelling Of Btex In Various Development Statuses In Penang, Malaysia.
by: Al Madhoun, Wesam Ahmed, et al.
Published: (2008)

A study on the concentration and dispersion of PM10 in UTHM by using simple modelling and meteorological factors
by: Abd Rahman, Malek Faizal
Published: (2013)

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study
by: Ku Yusof, Ku Mohd Kalkausar, et al.
Published: (2019)

Stochastic Generation Of Hourly Wind Speed Time Series.
by: A, Shamshad, et al.
Published: (2003)

Daily wind speed prediction in Kota Bharu using artificial neural network
by: Salam, Suhanawati
Published: (2007)

Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
by: Ahmat, Hasfazilah, et al.
Published: (2016)

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

A new framework for geospatial site selection using artificial neural networks as decision rules: a Case study on landfill sites
by: Abujayyab, Sohaib K. M., et al.
Published: (2015)

Indoor Concentrations of PM10 and Factors Influencing Its Concentrations in Day Care Centres in Bangkok, Thailand
by: Pudpong, N., et al.
Published: (2011)

Changes In Pm2.5 Levels After Covid-19 Outbreak In Malaysia
by: Razali, Wan Ahmad Asyraf Wan Mohd
Published: (2021)

Morphological and Elemental Analysis of Fineparticulate Matter (PM2.5) At Background Station in Malaysia
by: Mohd Zaki, Teh Nur Amalina
Published: (2016)

Elemental characteristics of particulate matter (Pm10 and Pm2.5) from peat swamp area in kuala pahang
by: Eng, Hwai Ern, et al.
Published: (2024)

Analysis of pm10 using extreme value theory / Hasfazilah Ahmat ... [et al.]
by: Ahmat, Hasfazilah, et al.
Published: (2015)