Implementing machine learning techniques to forecast floods In Bangladesh based on historical data

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Chi tiết về thư mục
Những tác giả chính:	Toufique, S.M, Bhuiyan, Sadiq Uddin, Lateef, Ahmed, Zaman, Arman, Islam, Jubaer Bin
Tác giả khác:	Ziaul Karim, Dewan
Định dạng:	Luận văn
Ngôn ngữ:	English
Được phát hành:	Brac University 2024
Những chủ đề:	Machine learning Flood prediction Natural calamities Deep learning
Truy cập trực tuyến:	http://hdl.handle.net/10361/22779

id	10361-22779
record_format	dspace
spelling	10361-227792024-05-08T21:01:22Z Implementing machine learning techniques to forecast floods In Bangladesh based on historical data Toufique, S.M Bhuiyan, Sadiq Uddin Lateef, Ahmed Zaman, Arman Islam, Jubaer Bin Ziaul Karim, Dewan Department of Computer Science and Engineering, Brac University Machine learning Flood prediction Natural calamities Deep learning Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-32). Flooding is a complex phenomenon that, due to its nonlinear and dynamic character, is difficult to anticipate. As a result, the prediction of floods has emerged as a critical area of study in the field of hydrology. Numerous researchers have handled this topic in various ways, spanning from physical models to image processing, however, the time steps and precision are insufficient for all applications. This report looks at machine learning approaches for forecasting weather conditions and criteria and assessing the related margins of uncertainty. The evaluated outputs enable more accurate and precise flood prediction for a variety of applications, including transportation systems. Through the exploration of innovative approaches to flood forecasting, machine learning algorithms have emerged as a potential solution. Up-and-coming methods, including ANNs, SVMs, and Random Forests, have shown impressive performance in identifying intricate patterns and connections in both weather and hydrological data. By leveraging past weather and water information, these algorithms can generate advanced predictions of future conditions and anticipate possible flood occurrences. Responding to emergency scenarios can be made more efficient and beneficial by exploiting machine learning capabilities and advanced sensor data to more accurately predict and prepare for the devastation caused by floods, and more easily deliver aid to flood affected regions. S.M Toufique Sadiq Uddin Bhuiyan Ahmed Lateef B.Sc. in Computer Science 2024-05-08T08:27:30Z 2024-05-08T08:27:30Z ©2024 2024-01 Thesis ID: 19201141 ID: 19201018 ID: 19241016 ID: 19201005 ID: 19341002 http://hdl.handle.net/10361/22779 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. 47 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning Flood prediction Natural calamities Deep learning Machine learning
spellingShingle	Machine learning Flood prediction Natural calamities Deep learning Machine learning Toufique, S.M Bhuiyan, Sadiq Uddin Lateef, Ahmed Zaman, Arman Islam, Jubaer Bin Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Ziaul Karim, Dewan
author_facet	Ziaul Karim, Dewan Toufique, S.M Bhuiyan, Sadiq Uddin Lateef, Ahmed Zaman, Arman Islam, Jubaer Bin
format	Thesis
author	Toufique, S.M Bhuiyan, Sadiq Uddin Lateef, Ahmed Zaman, Arman Islam, Jubaer Bin
author_sort	Toufique, S.M
title	Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
title_short	Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
title_full	Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
title_fullStr	Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
title_full_unstemmed	Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
title_sort	implementing machine learning techniques to forecast floods in bangladesh based on historical data
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22779
work_keys_str_mv	AT toufiquesm implementingmachinelearningtechniquestoforecastfloodsinbangladeshbasedonhistoricaldata AT bhuiyansadiquddin implementingmachinelearningtechniquestoforecastfloodsinbangladeshbasedonhistoricaldata AT lateefahmed implementingmachinelearningtechniquestoforecastfloodsinbangladeshbasedonhistoricaldata AT zamanarman implementingmachinelearningtechniquestoforecastfloodsinbangladeshbasedonhistoricaldata AT islamjubaerbin implementingmachinelearningtechniquestoforecastfloodsinbangladeshbasedonhistoricaldata
_version_	1814307746315501568

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data

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