Flood prediction using machine learning models

Flood prediction using machine learning models

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliografske podrobnosti
Main Authors:	Syeed, Miah Mohammad Asif, Farzana, Maisha, Namir, Ishadie, Ishrar, Ipshita, Nushra, Meherin Hossain
Drugi avtorji:	Rahman, Tanvir
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2022
Teme:	Binary logistic regression Support Vector Classifier(SVC) K-Nearest Neighbor(KNN) Decision Tree Classifier(DTC) Flood prediction Rainfall Machine learning Artificial intelligence
Online dostop:	http://hdl.handle.net/10361/16635

Podobne knjige/članki

Effectiveness of data mining in predicting heart diseases
od: Afrin, Shahria, et al.
Izdano: (2018)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
od: Rahee, Arnob, et al.
Izdano: (2021)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
od: Sarkar, Ripa, et al.
Izdano: (2024)

A feature selection approach to determine obesity using machine learning method
od: Antor, Shahidul Alam, et al.
Izdano: (2021)

A comparative study of car image generation quality using DCGAN and VSGAN
od: Shayer, Mirza Ahmad, et al.
Izdano: (2022)

Prediction of earthquakes: a step towards predicting the unpredictable
od: Chowdhury, Maheen, et al.
Izdano: (2023)

An approach to detect epileptic seizure using XAI and machine learning
od: Bijoy, Emam Hasan, et al.
Izdano: (2022)

The applications of data mining and machine learning in Bangladesh for disease pattern analysis and prediction
od: Mahmud, Mahmudul Hasan
Izdano: (2021)

Early grade prediction using profile data
od: Iqbal, Sumaiya, et al.
Izdano: (2021)

Enhancing crops production based on environmental status using machine learning techniques
od: Talukder, Shiyam, et al.
Izdano: (2021)

Flood prediction using ensemble machine learning models
od: Rahman, Tanvir
Izdano: (2024)

Detecting online recruitment fraud by using machine learning
od: Ghosh, Gitanjali, et al.
Izdano: (2021)

Identification of fake news using machine learning in distributed system
od: Saif, Mehruz, et al.
Izdano: (2021)

Cardiovascular disease prediction model using Machine Learning Algorithm
od: Arefin Mirdha, MD Shamsul
Izdano: (2023)

Sentiment analysis for Bengali newspaper headlines
od: Hossain, Mohammad Samman, et al.
Izdano: (2017)

Parking data analysis
od: Ahamed, Kasfi, et al.
Izdano: (2023)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
od: Alam, Auninda, et al.
Izdano: (2021)

EMG controlled bionic robotic arm using artificial intelligence and machine learning
od: Rupom, Farhan Fuad, et al.
Izdano: (2021)

Heart disease prediction system
od: Amit, Tayab Al Azad, et al.
Izdano: (2022)

Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
od: Hossain, Shoumik, et al.
Izdano: (2022)

Human recognition using wireless router signal
od: Chowdhury, S. M. A. Muksit, et al.
Izdano: (2019)

Email classification and meeting scheduling using classifier algorithm
od: Khan, Behroz Newaz, et al.
Izdano: (2017)

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
od: Mahmud, Booshra Nazifa, et al.
Izdano: (2019)

Data analysis on the Bangladesh share market using Machine-Learning
od: Sadman, Syed Hassan, et al.
Izdano: (2021)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
od: Saad-Ul-Kabir, Syed, et al.
Izdano: (2021)

Use of machine learning and IoT for monitoring and tracking of livestock
od: Aunindita, Rudaba Farhin, et al.
Izdano: (2023)

Best feature selection and data visualization for breast cancer prediction
od: Hemel, Tanjim Ahmed, et al.
Izdano: (2020)

Machine learning In breast cancer prognosis and prediction
od: Chowdhury, Shah Abul Hasnat, et al.
Izdano: (2023)

Performance analysis of machine learning algorithms for Malware classification
od: Bushra, Raisa Hasan, et al.
Izdano: (2023)

Assistive Guideline of Categorization for Competitive Programming Problems
od: Dhrubo, Najmus Sakib, et al.
Izdano: (2021)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
od: Farabe, Abdullah Al, et al.
Izdano: (2021)

Sentiment analysis to determine employee job satisfaction using machine learning techniques
od: Mouli, Nazifa, et al.
Izdano: (2023)

Analysis on IOT Botnet and DDOS attack
od: Nur, Md. Walid Bin
Izdano: (2018)

Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
od: Shrestha, Soptorsi Paul, et al.
Izdano: (2021)

Where will you setup your business next?: a machine learning approach to suggest ideal geographical location for new restaurant establishment
od: Shihab, Ibne Farabi, et al.
Izdano: (2018)

Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction
od: Chowdhury, Ahnaf Shahriyar, et al.
Izdano: (2021)

Emotion detection using EEG signals
od: Hossain, Mohammad Adnan
Izdano: (2021)

Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
od: Shourin, Bushra, et al.
Izdano: (2018)

Finding ideal geographical location for businesses using machine learning technique
od: Mahmud, Mir Ibtid, et al.
Izdano: (2022)

Prediction of rainfall using data mining techniques
od: Prema, Fahmida Tasnim, et al.
Izdano: (2018)