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Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection

Flooding is a natural disaster that annually destroys buildings, farmland, properties, and life in many regions of the world. Less than two decades ago, Topological data analysis (TDA) and machine learning (ML) were used in predictions, which have advantages over the common method. Thus, the present...

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Main Author: Obi, Ohanuba Felix
Format: Thesis
Language:English
Published: 2022
Subjects:
QA1 Mathematics (General)
Online Access:http://eprints.usm.my/60102/1/24%20Pages%20from%20OHANUBA%20FELIX%20OBI.pdf
http://eprints.usm.my/60102/
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spelling my.usm.eprints.60102 http://eprints.usm.my/60102/ Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection Obi, Ohanuba Felix QA1 Mathematics (General) Flooding is a natural disaster that annually destroys buildings, farmland, properties, and life in many regions of the world. Less than two decades ago, Topological data analysis (TDA) and machine learning (ML) were used in predictions, which have advantages over the common method. Thus, the present work introduces a hybrid method of TDA and unsupervised ML (TDA-uML) for flood management. The TDA-uML blends topological algebra with computer science to become a new study area in statistics, handling shapes in big data. Three properties make TDA distinct from common methods; they are coordinate invariance, deformation invariance, and compressed representation. The method involves training, testing, computation, obtaining of optimal values and validation of optimal value. Some common flood management models such as Hydrologic, hydraulic, and statistical models that researchers had used are inaccurate in the prediction, costly, lack the implementation of hybrid models, and are not validated compared to the TDA-uML method. The technique is aimed at developing a hybrid method of TDA-uML for flood prediction; evaluating the accuracy of the hybrid method (TDA-uML) in predicting flood, choosing the best validity tests for the study, and determining whether there is a relationship in the feature patterns. 2022-08 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60102/1/24%20Pages%20from%20OHANUBA%20FELIX%20OBI.pdf Obi, Ohanuba Felix (2022) Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection. PhD thesis, Perpustakaan Hamzah Sendut.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic QA1 Mathematics (General)
spellingShingle QA1 Mathematics (General)
Obi, Ohanuba Felix
Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
description Flooding is a natural disaster that annually destroys buildings, farmland, properties, and life in many regions of the world. Less than two decades ago, Topological data analysis (TDA) and machine learning (ML) were used in predictions, which have advantages over the common method. Thus, the present work introduces a hybrid method of TDA and unsupervised ML (TDA-uML) for flood management. The TDA-uML blends topological algebra with computer science to become a new study area in statistics, handling shapes in big data. Three properties make TDA distinct from common methods; they are coordinate invariance, deformation invariance, and compressed representation. The method involves training, testing, computation, obtaining of optimal values and validation of optimal value. Some common flood management models such as Hydrologic, hydraulic, and statistical models that researchers had used are inaccurate in the prediction, costly, lack the implementation of hybrid models, and are not validated compared to the TDA-uML method. The technique is aimed at developing a hybrid method of TDA-uML for flood prediction; evaluating the accuracy of the hybrid method (TDA-uML) in predicting flood, choosing the best validity tests for the study, and determining whether there is a relationship in the feature patterns.
format Thesis
author Obi, Ohanuba Felix
author_facet Obi, Ohanuba Felix
author_sort Obi, Ohanuba Felix
title Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
title_short Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
title_full Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
title_fullStr Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
title_full_unstemmed Topological Data Analysis Via Unsupervised Machine Learning For Recognizing Atmospheric Rivers Conditions On Flood Detection
title_sort topological data analysis via unsupervised machine learning for recognizing atmospheric rivers conditions on flood detection
publishDate 2022
url http://eprints.usm.my/60102/1/24%20Pages%20from%20OHANUBA%20FELIX%20OBI.pdf
http://eprints.usm.my/60102/
_version_ 1794552243534430208
score 13.211869

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