Prediction of earthquakes: a step towards predicting the unpredictable

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

書誌詳細
主要な著者:	Chowdhury, Maheen, Rahaman, Md Abdur, Islam, Tafhim Sadman, Sultana, Rahanuma, Rahman, Anjolika
その他の著者:	Mostakim, Moin
フォーマット:	学位論文
言語:	English
出版事項:	Brac University 2023
主題:	Earthquake K-Nearest Neighbors(KNN) Support Vector Machine(SVM) Extreme gradient Boosting(XGBOOST) Adaptive boosting(ADABOOST) Prediction/forecasting Machine learning
オンライン･アクセス:	http://hdl.handle.net/10361/21174

id	10361-21174
record_format	dspace
spelling	10361-211742023-09-24T21:11:27Z Prediction of earthquakes: a step towards predicting the unpredictable Chowdhury, Maheen Rahaman, Md Abdur Islam, Tafhim Sadman Sultana, Rahanuma Rahman, Anjolika Mostakim, Moin Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Earthquake K-Nearest Neighbors(KNN) Support Vector Machine(SVM) Extreme gradient Boosting(XGBOOST) Adaptive boosting(ADABOOST) Prediction/forecasting Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-49). One of the most catastrophic natural disasters is an earthquake, especially because they typically occur without warning. It has catastrophic effects on both the econ- omy of a nation and human life. Our paper is a step towards taking the challenge and complexity of predicting earthquakes and implies that the research here aims to make progress in this field. In this paper, we have proposed a hybrid model com- bining multiple algorithms which analyses already existing datasets. We have done our work in two steps. Firstly, we trained our model on multiple algorithms such as KNN, SVM, XGBOOST and ADABOOST. Then we created a hybrid model out of these algorithms which gave us the best results in terms of accuracy and precision. The use of machine learning techniques for earthquake prediction is examined in this thesis. We concentrate on the use of multiple algorithms: k-Nearest Neighbors (KNN), AdaBoost, and Support Vector Machines (SVM). We start by going over the state of earthquake forecasting right now and how machine learning is applied in this area. We next go over our study, which involves feeding our models input fea- tures made up of both seismological and geodetic data. We assess each algorithm’s performance using a range of evaluation indicators and contrast the outcomes with conventional statistical techniques. We explore the significance of our results for future research in this field and show how these machine learning techniques have the potential to be used to forecast earthquakes. Overall, this thesis makes a posi- tive contribution to the current work to increase the precision and dependability of earthquake prediction utilizing cutting-edge machine learning methods. Maheen Chowdhury Md Abdur Rahaman Tafhim Sadman Islam Rahanuma Sultana Anjolika Rahman B. Computer Science and Engineering 2023-09-24T06:03:54Z 2023-09-24T06:03:54Z 2023 2023-01 Thesis ID 18201069 ID 18201093 ID 18301220 ID 18201105 ID 21101347 http://hdl.handle.net/10361/21174 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. 49 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Earthquake K-Nearest Neighbors(KNN) Support Vector Machine(SVM) Extreme gradient Boosting(XGBOOST) Adaptive boosting(ADABOOST) Prediction/forecasting Machine learning
spellingShingle	Earthquake K-Nearest Neighbors(KNN) Support Vector Machine(SVM) Extreme gradient Boosting(XGBOOST) Adaptive boosting(ADABOOST) Prediction/forecasting Machine learning Chowdhury, Maheen Rahaman, Md Abdur Islam, Tafhim Sadman Sultana, Rahanuma Rahman, Anjolika Prediction of earthquakes: a step towards predicting the unpredictable
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Chowdhury, Maheen Rahaman, Md Abdur Islam, Tafhim Sadman Sultana, Rahanuma Rahman, Anjolika
format	Thesis
author	Chowdhury, Maheen Rahaman, Md Abdur Islam, Tafhim Sadman Sultana, Rahanuma Rahman, Anjolika
author_sort	Chowdhury, Maheen
title	Prediction of earthquakes: a step towards predicting the unpredictable
title_short	Prediction of earthquakes: a step towards predicting the unpredictable
title_full	Prediction of earthquakes: a step towards predicting the unpredictable
title_fullStr	Prediction of earthquakes: a step towards predicting the unpredictable
title_full_unstemmed	Prediction of earthquakes: a step towards predicting the unpredictable
title_sort	prediction of earthquakes: a step towards predicting the unpredictable
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/21174
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Prediction of earthquakes: a step towards predicting the unpredictable

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