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Machine Learning Approach to Global and Hemispheres Mean Temperature Anomalies Predictions with Artificial Neural Networks (ANNs)

by Farah Yasmeen, Iqra Khalid
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 40
Year of Publication: 2022
Authors: Farah Yasmeen, Iqra Khalid
10.5120/ijca2022922506

Farah Yasmeen, Iqra Khalid . Machine Learning Approach to Global and Hemispheres Mean Temperature Anomalies Predictions with Artificial Neural Networks (ANNs). International Journal of Computer Applications. 184, 40 ( Dec 2022), 20-26. DOI=10.5120/ijca2022922506

@article{ 10.5120/ijca2022922506,
author = { Farah Yasmeen, Iqra Khalid },
title = { Machine Learning Approach to Global and Hemispheres Mean Temperature Anomalies Predictions with Artificial Neural Networks (ANNs) },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2022 },
volume = { 184 },
number = { 40 },
month = { Dec },
year = { 2022 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number40/32578-2022922506/ },
doi = { 10.5120/ijca2022922506 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:39.928666+05:30
%A Farah Yasmeen
%A Iqra Khalid
%T Machine Learning Approach to Global and Hemispheres Mean Temperature Anomalies Predictions with Artificial Neural Networks (ANNs)
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 40
%P 20-26
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the machine learning algorithm artificial neural network (ANN) model is applied to the Global, Northern Hemisphere and Southern Hemisphere mean temperature anomalies. The combined land-surface air and sea-surface water temperature data are obtained from Goddard Institute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere and Southern Hemisphere means since 1880 to present. The global temperature change is analyzed and the alternative analysis is compared for addressing the reality of global warming. The forecasts for the next ten years are obtained using two different ANN models; namely the NNAR (neural network auto-regression) and MLP (Multilayer perceptron) models. These forecasts are compared with Exponential Smoothing State Space (ETS) model, ARIMA/SARIMA and random walk (RW) models. The comparison is made on the basis of mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

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Index Terms

Computer Science
Information Sciences

Keywords

Weather forecasting machine learning hemispheres temperature temperature anomalies artificial neural network (ANN) multilayer perceptron

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