Predicting Temperature of Major Cities Using Machine Learning and Deep Learning

TL;DR

This paper explores machine learning and deep learning techniques, especially LSTM, for accurate temperature prediction of major cities, aiming to improve climate change forecasting and reduce reliance on traditional, costly weather prediction models.

Contribution

It introduces a methodology combining LSTM with ARIMA, SARIMA, and Prophet models for city temperature forecasting using time series data, enhancing prediction accuracy.

Findings

LSTM effectively captures long-term dependencies in temperature data.

The combined models improve prediction accuracy over traditional methods.

Seasonality and stationarity analysis aid in understanding recurring temperature patterns.

Abstract

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.