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Forecasting the Average Temperature Rise in Bangladesh: a Time Series Analysis

Sneha Paul*
Dept. of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna-9203

Shuvendu Roy
Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203

Abstract/ Executive Summary:

Global warming has caused a significant increment in surface temperature around the world, including Bangladesh. In this study, the temperature data of Bangladesh over the past 100 years has been analyzed to see the temperature increment pattern. It has been seen that the average temperature has risen by 1⁰C over the last century. Using daily average temperature data of Bangladesh, machine learning-based time series forecasting model has been developed to predict the future temperature of Bangladesh. The model can predict the minimum, maximum, and average temperatures of any year in the future. This has been treated as a regression problem and Linear, Polynomial, and Support Vector Regression have been proposed to build the prediction model. The proposed model has a mean square error of 0.0047⁰C which is a good margin for such a model. Using the model, the average temperature of Bangladesh is predicted over the next hundred years.

Keywords: Global warming; Climate Change; Average Temperature rise; Prediction; Bangladesh.

Location: In Bangladesh

Start Date:

End Date:

Program Area: Risk Management in Agriculture

Commodity: Temperature

Objectives:

In this study, the past 100 years' temperature rise data of Bangladesh has been analyzed to find out the temperature rise trend for this region. Three models: linear regression model, polynomial regression model, support vector model (SVR) are developed to predict the average temperature rise in Bangladesh for the next 100 years.

Methodology:

This section describes the methodologies used to build the prediction model to forecast the temperature of Bangladesh in the future. The problem is treated as a regression task and experimented with different algorithms to build the best model for this application. Following algorithm is used in the experiments: (a) Linear Regression (b) Polynomial Regression (c) Support Vector Regression (SVR).

2.4 Data Description In this study, the Berkeley Earth Surface Temperature dataset has been used (Rohde, 2013). The dataset is constructed from popular land and ocean temperature datasets called NOAA's MLOST, NASA's GISTEMP, and UK's HadCrut by Lawrence Berkeley National Laboratory. The dataset contains 1.6 billion temperature reports from 16 pre-existing archives. It contains data from 1750 in average format and 1850 with minimum and maximum temperature. In our model, data from 1900 have been used as there are no missing data points since then. In the dataset, temperature data is considered for only one point (23.31N, 90.00E) in Bangladesh. This is a limitation of the study. With more points, the model will be able to predict temperature with more precision.

Source of Information: Journal of Engineering Science 11(1), 2020, 83-91

Article Link (Online Journal):

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In this study, the past 100 years temperature data of Bangladesh has been analyzed and it has been seen that in the past 100 years temperature of Bangladesh has risen by 1⁰C on average. Linear regression model, polynomial regression model and SVR are developed to predict the average temperature of each year in Bangladesh for the next 100 years. The performances of those models are compared, and it is found that the polynomial regression with degree 3 is the best model for this application. An increase of close to another 1⁰C can be seen in the average temperature of Bangladesh from the prediction of the model.

Knowledge Management: Journal