Islam, MD SaifulSyed, ShehranAnik, Marum MonemAlim, Sakib BinLucky, Rakebun IslamAhmed, Aunindya ArifNahian, Prethu2021-10-112021-10-1120212021-06ID 21141068ID 21141071ID 17101225ID 17301191http://hdl.handle.net/10361/15202Cataloged from PDF version of thesis.Includes bibliographical references (page 29-30).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Being a riverine country with more than 400 rivers, flood is a common phenomenon for Bangladesh. As, the land is less than five meters above sea level, and also due to heavy rainfall during monsoon season, it makes the country an easy target of flooding and about 30% of the total area is in danger level during this period. Additional to the yearly flooding, every 4 to 5 years there is a major flood occurs which covers more than 60% of the country. As of 22 July, 2020 alone, 102 upazila and 654 unions have been inundated in flood, affecting 3.3 million people, leaving 731,958 people water logged and a total of 93 deaths [2]. The aim of this research is to predict Bangladesh’s susceptibility to flooding so that the government as well as the people of this country can take necessary steps to lessen the effect. To predict the probability of flood we will be using some machine learning algorithm namely Linear Regression model, Random forest Regressor, Naive Bayes Theorem and Artificial Neural Network. This study is based on the data set from 1991-2013 water level and weather variables from Khulna districts Rupsa-Pasur station.30 pagesenBrac 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.Flood SusceptibilityMachine LearningFlood in BangladeshLinear Regression Model and Random forestNaive Bayes TheoremArtificial Neural NetworkMachine LearningThesis