Predicting temperature of major cities using machine learning and deep learning

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

Bibliografski detalji
Glavni autori:	Jaharabi, Wasiou, Hossain, MD Ibrahim Al, Tahmid, Rownak, Islam, Md. Zuhayer, Rayhan, T.M. Saad
Daljnji autori:	Shakil, Arif
Format:	Disertacija
Jezik:	English
Izdano:	Brac University 2024
Teme:	Predicting temperature Time series analysis Recurrent neural networks Long short term memory networks Machine learning Cognitive learning theory
Online pristup:	http://hdl.handle.net/10361/23616

id	10361-23616
record_format	dspace
spelling	10361-236162024-06-27T21:02:17Z Predicting temperature of major cities using machine learning and deep learning Jaharabi, Wasiou Hossain, MD Ibrahim Al Tahmid, Rownak Islam, Md. Zuhayer Rayhan, T.M. Saad Shakil, Arif Department of Computer Science and Engineering, Brac University Predicting temperature Time series analysis Recurrent neural networks Long short term memory networks Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-37). Currently, the issue that concerns the world leaders most is climate change for its effect on agriculture, environment and economies of daily life. So, to combat this, temperature prediction with strong accuracy is vital. So far, the most effective widely used measure for such forecasting is Numerical weather prediction (NWP) which is a mathematical model that needs broad data from different applications to make predictions. This expensive, time and labor consuming work can be minimized through making such predictions using Machine learning algorithms. Using the database made by University of Dayton which consists the change of temperature in major cities we used the Time Series Analysis method where we use LSTM for the purpose of turning existing data into a tool for future prediction. LSTM takes the long-term data as well as any short-term exceptions or anomalies that may have occurred and calculates trend, seasonality and the stationarity of a data. By using models such as ARIMA, SARIMA, Prophet with the concept of RNN and LSTM we can, filter out any abnormalities, preprocess the data compare it with previous trends and make a prediction of future trends. Also, seasonality and stationarity help us analyze the reoccurrence or repeat over one year variable and removes the constrain of time in which the data was dependent so see the general changes that are predicted. By doing so we managed to make prediction of the temperature of different cities during any time in future based on available data and built a method of accurate prediction. This document contains our methodology for being able to make such predictions. Wasiou Jaharabi MD Ibrahim Al Hossain Rownak Tahmid Md. Zuhayer Islam T.M. Saad Rayhan B.Sc in Computer Science 2024-06-27T04:15:38Z 2024-06-27T04:15:38Z 2022 2022-05 Thesis ID 18101180 ID 18101076 ID 18101671 ID 18101334 ID 18101309 http://hdl.handle.net/10361/23616 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. 37 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Predicting temperature Time series analysis Recurrent neural networks Long short term memory networks Machine learning Cognitive learning theory
spellingShingle	Predicting temperature Time series analysis Recurrent neural networks Long short term memory networks Machine learning Cognitive learning theory Jaharabi, Wasiou Hossain, MD Ibrahim Al Tahmid, Rownak Islam, Md. Zuhayer Rayhan, T.M. Saad Predicting temperature of major cities using machine learning and deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Shakil, Arif
author_facet	Shakil, Arif Jaharabi, Wasiou Hossain, MD Ibrahim Al Tahmid, Rownak Islam, Md. Zuhayer Rayhan, T.M. Saad
format	Thesis
author	Jaharabi, Wasiou Hossain, MD Ibrahim Al Tahmid, Rownak Islam, Md. Zuhayer Rayhan, T.M. Saad
author_sort	Jaharabi, Wasiou
title	Predicting temperature of major cities using machine learning and deep learning
title_short	Predicting temperature of major cities using machine learning and deep learning
title_full	Predicting temperature of major cities using machine learning and deep learning
title_fullStr	Predicting temperature of major cities using machine learning and deep learning
title_full_unstemmed	Predicting temperature of major cities using machine learning and deep learning
title_sort	predicting temperature of major cities using machine learning and deep learning
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23616
work_keys_str_mv	AT jaharabiwasiou predictingtemperatureofmajorcitiesusingmachinelearninganddeeplearning AT hossainmdibrahimal predictingtemperatureofmajorcitiesusingmachinelearninganddeeplearning AT tahmidrownak predictingtemperatureofmajorcitiesusingmachinelearninganddeeplearning AT islammdzuhayer predictingtemperatureofmajorcitiesusingmachinelearninganddeeplearning AT rayhantmsaad predictingtemperatureofmajorcitiesusingmachinelearninganddeeplearning
_version_	1814308096093192192

Predicting temperature of major cities using machine learning and deep learning

Similar Items