Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria

Flood is a natural disaster that has become a major concern to the Nigerian government. Despite the numerous hazards caused by the flood, little attention has been directed towards evaluating the flood hazards through the river condition and vulnerability components along the river areas. Hence, thi...

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Main Author:	Ayinla, Chindo Abdulrasaq
Format:	Thesis
Language:	eng eng eng eng
Published:	2021
Subjects:	DT Africa QE Geology
Online Access:	https://etd.uum.edu.my/9335/1/s901651_01.pdf https://etd.uum.edu.my/9335/2/s901651_02.pdf https://etd.uum.edu.my/9335/3/s901651_references.docx https://etd.uum.edu.my/9335/5/depositpermission_s901651.pdf
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id	my-uum-etd.9335
record_format	uketd_dc
spelling	my-uum-etd.93352022-05-09T03:26:47Z Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria 2021 Ayinla, Chindo Abdulrasaq Mohd Shaharanee, IzwanNizal Mohd Jamil, Jastini Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences DT Africa QE Geology Flood is a natural disaster that has become a major concern to the Nigerian government. Despite the numerous hazards caused by the flood, little attention has been directed towards evaluating the flood hazards through the river condition and vulnerability components along the river areas. Hence, this study examines the river condition and vulnerability components to determine the cross-sectional variables in predicting the magnitude of flood along Foma River areas. Data extracted from Geographic Information System (GIS) and site observations were used in generating the cross-sectional variables along the river areas. From the dataset, eight crosssectional variables were obtained including 530 structures of Foma River. The Ordered Logit Regression (OLR) Models were built to predict the magnitude of flood. The model was evaluated using average values of accuracy, precision, recall, and F1-score which were derived from the 10-fold cross validation procedure. The F1-score was able to harmonize and reduce the errors in regulating the imbalanced class distributions. It was also revealed that river watersheds, structure vulnerable status, vulnerable structures along the river, locations of bridges and culverts, sizes occupied by bridges and culverts, and river pollution are significantly contributing to the magnitude of the flood along the Foma River. This study produced a complementary approach to flood prediction along the Foma River, as well as provided the Nigerian government and practitioners a new source of information in addressing problems related to river flooding in Nigeria. 2021 Thesis https://etd.uum.edu.my/9335/ https://etd.uum.edu.my/9335/1/s901651_01.pdf text eng 2024-03-25 staffonly https://etd.uum.edu.my/9335/2/s901651_02.pdf text eng public https://etd.uum.edu.my/9335/3/s901651_references.docx text eng public https://etd.uum.edu.my/9335/5/depositpermission_s901651.pdf text eng staffonly other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng eng
advisor	Mohd Shaharanee, IzwanNizal Mohd Jamil, Jastini
topic	DT Africa QE Geology
spellingShingle	DT Africa QE Geology Ayinla, Chindo Abdulrasaq Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
description	Flood is a natural disaster that has become a major concern to the Nigerian government. Despite the numerous hazards caused by the flood, little attention has been directed towards evaluating the flood hazards through the river condition and vulnerability components along the river areas. Hence, this study examines the river condition and vulnerability components to determine the cross-sectional variables in predicting the magnitude of flood along Foma River areas. Data extracted from Geographic Information System (GIS) and site observations were used in generating the cross-sectional variables along the river areas. From the dataset, eight crosssectional variables were obtained including 530 structures of Foma River. The Ordered Logit Regression (OLR) Models were built to predict the magnitude of flood. The model was evaluated using average values of accuracy, precision, recall, and F1-score which were derived from the 10-fold cross validation procedure. The F1-score was able to harmonize and reduce the errors in regulating the imbalanced class distributions. It was also revealed that river watersheds, structure vulnerable status, vulnerable structures along the river, locations of bridges and culverts, sizes occupied by bridges and culverts, and river pollution are significantly contributing to the magnitude of the flood along the Foma River. This study produced a complementary approach to flood prediction along the Foma River, as well as provided the Nigerian government and practitioners a new source of information in addressing problems related to river flooding in Nigeria.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Ayinla, Chindo Abdulrasaq
author_facet	Ayinla, Chindo Abdulrasaq
author_sort	Ayinla, Chindo Abdulrasaq
title	Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
title_short	Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
title_full	Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
title_fullStr	Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
title_full_unstemmed	Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria
title_sort	ordered logit regression model for predicting magnitude of flood along foma river kwara nigeria
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2021
url	https://etd.uum.edu.my/9335/1/s901651_01.pdf https://etd.uum.edu.my/9335/2/s901651_02.pdf https://etd.uum.edu.my/9335/3/s901651_references.docx https://etd.uum.edu.my/9335/5/depositpermission_s901651.pdf
_version_	1747828575794364416

Ordered Logit regression model for predicting magnitude of flood along Foma River Kwara Nigeria

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