Performance comparison of haze prediction using chaos theory and multiple linear regression
Forecasting haze is essential for protecting the environment, the economy, and public health. It assists authorities in taking preventative action to lessen the adverse effects of haze episodes and boost community resistance to air pollution. The goal of this study was to create a model for haze pre...
| Main Authors: | Hazlina Darman, Nor Zila Abd Hamid |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
|
| Online Access: | http://journalarticle.ukm.my/25174/ http://journalarticle.ukm.my/25174/1/23-34%20Paper.pdf |
Similar Items
Predicting haze phenomenon using chaos theory in industrial area in malaysia
by: Hazlina Darman,, et al.
Published: (2024)
by: Hazlina Darman,, et al.
Published: (2024)
Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees
by: Hamid, Nur Haziqah Mohd
Published: (2017)
by: Hamid, Nur Haziqah Mohd
Published: (2017)
Comparison between ANN and multiple linear regression models for prediction of warranty cost
by: Mohd Faaizie, Darmawan, et al.
Published: (2018)
by: Mohd Faaizie, Darmawan, et al.
Published: (2018)
Comparison between ANN and multiple linear regression models for prediction of warranty cost
by: Darmawan, Mohd Faaizie, et al.
Published: (2018)
by: Darmawan, Mohd Faaizie, et al.
Published: (2018)
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia
by: Abdullah, Samsuri, et al.
Published: (2020)
by: Abdullah, Samsuri, et al.
Published: (2020)
Comparison of chaos optimization functions for performance improvement of fitting of non-linear geometries
by: Moroni, Giovanni, et al.
Published: (2016)
by: Moroni, Giovanni, et al.
Published: (2016)
Comparison between fuzzy bootstrap weighted multiple linear regression and
multiple linear regression: a case study for oral cancer modelling
by: Mohd Ibrahim, Mohamad Shafiq, et al.
Published: (2018)
by: Mohd Ibrahim, Mohamad Shafiq, et al.
Published: (2018)
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)
by: Ku Yusof, Ku Mohd Kalkausar, et al.
Published: (2019)
JMASM 46: Algorithm for comparison of robust regression methods in multiple linear regression by weighting least Square Regression
by: Mohd Ibrahim, Mohamad Shafiq, et al.
Published: (2017)
by: Mohd Ibrahim, Mohamad Shafiq, et al.
Published: (2017)
Multiple outliers detection procedures in linear regression
by: Adnan, Robiah, et al.
Published: (2003)
by: Adnan, Robiah, et al.
Published: (2003)
Switching control of linear systems for generating chaos
by: Liu, X., et al.
Published: (2006)
by: Liu, X., et al.
Published: (2006)
On rare mutation, chaos and Darwin’s theory
by: Ganikhodjaev, Nasir, et al.
Published: (2014)
by: Ganikhodjaev, Nasir, et al.
Published: (2014)
Diurnal fluctuations of ozone concentrations and its precursors and prediction of ozone using multiple linear regressions
by: Nor Azam Ramli,, et al.
Published: (2010)
by: Nor Azam Ramli,, et al.
Published: (2010)
Prediction Of PM10 Concentration Using Multiple Linear Regression And Support Vector Machine
by: Zailan, Masezatti
Published: (2018)
by: Zailan, Masezatti
Published: (2018)
Prediction Of PM10 Concentration Using Multiple Linear Regression And Bayesian Model Averaging
by: Ismail, Hafizahizzati
Published: (2017)
by: Ismail, Hafizahizzati
Published: (2017)
Prediction of pm10 concentration using multiple linear regression and support vector machine
by: Zailan, Masezatti
Published: (2018)
by: Zailan, Masezatti
Published: (2018)
Comparison between multiple regression and multivariate adaptive regression splines for predicting CO2 emissions in ASEAN countries
by: Tay, Sze Hui, et al.
Published: (2013)
by: Tay, Sze Hui, et al.
Published: (2013)
A predictive model for the population growth of refugees in Asia: a multiple linear regression approach
by: Sulaiman, Suriani, et al.
Published: (2019)
by: Sulaiman, Suriani, et al.
Published: (2019)
Multiple linear regression model analysis in predicting fasting blood glucose level in healthy subjects
by: Aishah, A. F. Q. A., et al.
Published: (2019)
by: Aishah, A. F. Q. A., et al.
Published: (2019)
Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability
by: Fridgeirsdottir, Gudrun, et al.
Published: (2018)
by: Fridgeirsdottir, Gudrun, et al.
Published: (2018)
Identifying multiple outliers in linear regression :
Robust fit and clustering approach
by: Adnan, Robiah, et al.
Published: (2001)
by: Adnan, Robiah, et al.
Published: (2001)
Multiple Linear Regression for Predicting the Ship Booking Time:
A Case Study at PT. Samudera Indonesia
by: Tri Basuki, Kurniawan, et al.
Published: (2023)
by: Tri Basuki, Kurniawan, et al.
Published: (2023)
Distributionally robust L1-estimation in multiple linear regression
by: Gong, Z., et al.
Published: (2018)
by: Gong, Z., et al.
Published: (2018)
Cluster-Based Estimators For
Multiple And Multivariate Linear
Regression Models
by: Alih, Ekele
Published: (2015)
by: Alih, Ekele
Published: (2015)
Determinants of gold price: using simple and multiple linear regression
by: Choong, Pik San, et al.
Published: (2012)
by: Choong, Pik San, et al.
Published: (2012)
Detection of multiple outliners in linear regression using nonparametric methods
by: Adnan, Robiah
Published: (2004)
by: Adnan, Robiah
Published: (2004)
Forecasting gold prices using multiple linear regression method
by: Ismail, Zuhaimy, et al.
Published: (2009)
by: Ismail, Zuhaimy, et al.
Published: (2009)
The performance of clustering approach with robust mm-estimator for multiple outlier detection in linear regression
by: Mohd. Azmi, Nurulhuda Firdaus, et al.
Published: (2006)
by: Mohd. Azmi, Nurulhuda Firdaus, et al.
Published: (2006)
Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model
by: Qurratu 'Aini Aishah, Ahmad Fazil, et al.
Published: (2021)
by: Qurratu 'Aini Aishah, Ahmad Fazil, et al.
Published: (2021)
Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model
by: Qurratu 'Aini Aishah, Ahmad Fazil
Published: (2021)
by: Qurratu 'Aini Aishah, Ahmad Fazil
Published: (2021)
Subterahertz chaos generation by coupling a superlattice to a linear resonator
by: Hramov, A.E., et al.
Published: (2014)
by: Hramov, A.E., et al.
Published: (2014)
Multiple linear regression modelling of hot water extraction of soursop juice
by: Quek, Meei Chien, et al.
Published: (2012)
by: Quek, Meei Chien, et al.
Published: (2012)
The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression
by: Midi, Habshah, et al.
Published: (2009)
by: Midi, Habshah, et al.
Published: (2009)
Support vector regression with chaos-based firefly algorithm for stock market price forecasting
by: Kazem, A., et al.
Published: (2012)
by: Kazem, A., et al.
Published: (2012)
Chaos Theory: Implications for Cost Overrun Research in Hydrocarbon Megaprojects
by: Olaniran, O., et al.
Published: (2017)
by: Olaniran, O., et al.
Published: (2017)
The 'unimaginable' chaos continues
by: Abd Razak, Dzulkifli
Published: (2006)
by: Abd Razak, Dzulkifli
Published: (2006)
Comparison of physical and chemical properties of ambient aerosols during the 2009 haze and non-haze periods in Southeast Asia
by: Xu, Jingsha, et al.
Published: (2015)
by: Xu, Jingsha, et al.
Published: (2015)
A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
by: Shafi, Muhammad Ammar, et al.
Published: (2019)
by: Shafi, Muhammad Ammar, et al.
Published: (2019)
JMASM39: Algorithm for combining robust and bootstrap in multiple linear model regression
by: Wan Ahmad, Wan Muhamad Amir, et al.
Published: (2016)
by: Wan Ahmad, Wan Muhamad Amir, et al.
Published: (2016)
Fast improvised influential distance for the identification of influential observations in multiple linear regression
by: Habshah Midi,, et al.
Published: (2021)
by: Habshah Midi,, et al.
Published: (2021)
Similar Items
-
Predicting haze phenomenon using chaos theory in industrial area in malaysia
by: Hazlina Darman,, et al.
Published: (2024) -
Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees
by: Hamid, Nur Haziqah Mohd
Published: (2017) -
Comparison between ANN and multiple linear regression models for prediction of warranty cost
by: Mohd Faaizie, Darmawan, et al.
Published: (2018) -
Comparison between ANN and multiple linear regression models for prediction of warranty cost
by: Darmawan, Mohd Faaizie, et al.
Published: (2018) -
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia
by: Abdullah, Samsuri, et al.
Published: (2020)