An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly

Malaysia experiences transboundary haze episodes in which the air contains particulate matter (PM) that is harmful to human health and the environment. Therefore, the main prediction model used in this study is Boosted Regression Trees (BRT) to predict three days ahead of PM10 concentration. However...

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Main Author:	Wan Mohd Rosly, Wan Nur Shaziayani
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Atmospheric temperature
Online Access:	https://ir.uitm.edu.my/id/eprint/66923/1/66923.pdf
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id	my-uitm-ir.66923
record_format	uketd_dc
spelling	my-uitm-ir.669232023-05-16T07:53:54Z An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly 2022 Wan Mohd Rosly, Wan Nur Shaziayani Atmospheric temperature Malaysia experiences transboundary haze episodes in which the air contains particulate matter (PM) that is harmful to human health and the environment. Therefore, the main prediction model used in this study is Boosted Regression Trees (BRT) to predict three days ahead of PM10 concentration. However, the main problem with the common BRT technique is that it is not suitable for use in predicting extreme values of PM10 concentration levels. Besides, the problem with BRT is that overfitting can occur if the number of trees is not suitable and also because of the complexity of the model, which is caused by the unsuitable number of predictor variables used in the model. Therefore, the aim of this study is to enhance the BRT model with Quantile Regression (QR) and Support Vector Machine (SVM) weight. This study used maximum daily monitoring records from 2002 to 2017 in Alor Setar, Klang, and Kuching which were analysed using four models: a boosted regression tree (BRT) model, a BRT with QR loss function model and a hybrid model between SVM and BRT with and without QR loss function. In order to get the best prediction model and to avoid over-fitting, the number of trees (nt) was optimized by using independent test set (TEST), cross validation (CV) and out of bag estimation (OOB). Then, to solve the extreme value issue in BRT, this study used the QR loss function rather than the Ordinary Least Square (OLS) loss function, since QR is more resistant to outliers. Meanwhile, the model then evaluated and the best method for predicting PM10 concentration was selected based on the lowest error and highest accuracy values. 2022 Thesis https://ir.uitm.edu.my/id/eprint/66923/ https://ir.uitm.edu.my/id/eprint/66923/1/66923.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Mohamad Japeri, Ahmad Zia Ul-Saufie
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohamad Japeri, Ahmad Zia Ul-Saufie
topic	Atmospheric temperature
spellingShingle	Atmospheric temperature Wan Mohd Rosly, Wan Nur Shaziayani An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
description	Malaysia experiences transboundary haze episodes in which the air contains particulate matter (PM) that is harmful to human health and the environment. Therefore, the main prediction model used in this study is Boosted Regression Trees (BRT) to predict three days ahead of PM10 concentration. However, the main problem with the common BRT technique is that it is not suitable for use in predicting extreme values of PM10 concentration levels. Besides, the problem with BRT is that overfitting can occur if the number of trees is not suitable and also because of the complexity of the model, which is caused by the unsuitable number of predictor variables used in the model. Therefore, the aim of this study is to enhance the BRT model with Quantile Regression (QR) and Support Vector Machine (SVM) weight. This study used maximum daily monitoring records from 2002 to 2017 in Alor Setar, Klang, and Kuching which were analysed using four models: a boosted regression tree (BRT) model, a BRT with QR loss function model and a hybrid model between SVM and BRT with and without QR loss function. In order to get the best prediction model and to avoid over-fitting, the number of trees (nt) was optimized by using independent test set (TEST), cross validation (CV) and out of bag estimation (OOB). Then, to solve the extreme value issue in BRT, this study used the QR loss function rather than the Ordinary Least Square (OLS) loss function, since QR is more resistant to outliers. Meanwhile, the model then evaluated and the best method for predicting PM10 concentration was selected based on the lowest error and highest accuracy values.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wan Mohd Rosly, Wan Nur Shaziayani
author_facet	Wan Mohd Rosly, Wan Nur Shaziayani
author_sort	Wan Mohd Rosly, Wan Nur Shaziayani
title	An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
title_short	An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
title_full	An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
title_fullStr	An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
title_full_unstemmed	An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly
title_sort	enhanced boosted regression tree model for the prediction of pm10 concentration level using svm_brt with qr loss function coupling approach / wan nur shaziayani wan mohd rosly
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/66923/1/66923.pdf
_version_	1783735647060623360

An enhanced boosted regression tree model for the prediction of PM10 concentration level using SVM_BRT with QR loss function coupling approach / Wan Nur Shaziayani Wan Mohd Rosly

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