Flood prediction using ensemble machine learning models

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.

Manylion Llyfryddiaeth
Prif Awdur:	Rahman, Tanvir
Awduron Eraill:	Alam,Golam Rabiul
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	2024
Pynciau:	Floods Machine learning Binary logistic regression Stacked generalization model Regression analysis > Data processing
Mynediad Ar-lein:	http://hdl.handle.net/10361/22984

id	10361-22984
record_format	dspace
spelling	10361-229842024-05-29T21:04:38Z Flood prediction using ensemble machine learning models Rahman, Tanvir Alam,Golam Rabiul Alam, A. M. Esfar-E Department of Computer Science and Engineering, Brac University Floods Machine learning Binary logistic regression Stacked generalization model Machine learning Regression analysis--Data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-35). Frequent and devastating floods in India pose a significant threat to people and property. Accurate and real-time forecasting of floods is essential to mitigate their impact. This thesis focuses on evaluating di↵erent machine learning models for flood prediction in India. The models assessed include K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree Classifier, Binary Logistic Regression, and Stacked Generalization (Stacking). The researchers trained and tested these models using a rainfall dataset. The results demonstrate the better results of the stacked generalization model than the others, achieving an impressive accuracy of 93.3 per cent with a standard deviation(sd) of 0.098. These findings highlight the potential of machine learning models to provide precise and timely flood predictions, empowering the local authorities, specially disaster management ones, to take necessary actions to avoid destruction and preferably save people. Tanvir Rahman M.Sc. in Computer Science and Engineering 2024-05-29T05:45:44Z 2024-05-29T05:45:44Z ©2023 2023-07 Thesis ID 20166052 http://hdl.handle.net/10361/22984 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 44 pages application/pdf
institution	Brac University
collection	Institutional Repository
language	English
topic	Floods Machine learning Binary logistic regression Stacked generalization model Machine learning Regression analysis--Data processing
spellingShingle	Floods Machine learning Binary logistic regression Stacked generalization model Machine learning Regression analysis--Data processing Rahman, Tanvir Flood prediction using ensemble machine learning models
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
author2	Alam,Golam Rabiul
author_facet	Alam,Golam Rabiul Rahman, Tanvir
format	Thesis
author	Rahman, Tanvir
author_sort	Rahman, Tanvir
title	Flood prediction using ensemble machine learning models
title_short	Flood prediction using ensemble machine learning models
title_full	Flood prediction using ensemble machine learning models
title_fullStr	Flood prediction using ensemble machine learning models
title_full_unstemmed	Flood prediction using ensemble machine learning models
title_sort	flood prediction using ensemble machine learning models
publishDate	2024
url	http://hdl.handle.net/10361/22984
work_keys_str_mv	AT rahmantanvir floodpredictionusingensemblemachinelearningmodels
_version_	1814309234109579264

Flood prediction using ensemble machine learning models

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