Utilising key climate element variability for the prediction of future climate change using a support vector machine model

This paper proposes a support vector machine (SVM) model to advance the prediction accuracy of global land-ocean temperature (GLOT), which is globally significant for understanding the future pattern of climate change. The GLOT dataset was collected from NASA’s GLOT index (C) (anomaly with base:...

Full description

Bibliographic Details
Main Authors:	Abubakar, Adamu, Haruna, Chiroma, Zeki, Akram M., Uddin, Mueen
Format:	Article
Language:	English English
Published:	Inderscience Enterprises Ltd 2016
Subjects:	Q350 Information theory
Online Access:	http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/1/IJGW090201_ABUBAKAR.pdf http://irep.iium.edu.my/49995/4/49995_Utilising%20key%20climate%20element%20variability%20for%20the%20prediction%20of%20future%20climate_Scopus.pdf

id	iium-49995
recordtype	eprints
spelling	iium-499952017-04-05T07:04:34Z http://irep.iium.edu.my/49995/ Utilising key climate element variability for the prediction of future climate change using a support vector machine model Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Uddin, Mueen Q350 Information theory This paper proposes a support vector machine (SVM) model to advance the prediction accuracy of global land-ocean temperature (GLOT), which is globally significant for understanding the future pattern of climate change. The GLOT dataset was collected from NASA’s GLOT index (C) (anomaly with base: 1951–1980) for the period 1880 to 2013. We categorise the dataset by decades to describe the behaviour of the GLOT within those decades. The dataset was used to build an SVM Model to predict future values of the GLOT. The performance of the model was compared with a multilayer perceptron neural network (MLPNN) and validated statistically. The SVM was found to perform significantly better than the MLPNN in terms of mean square error and root mean square error, although computational times for the two models are statistically equal. The SVM model was used to project the GLOT from the pre-existing NASA’s GLOT index (C) (anomaly with base: 1951–1980) for the next 20 years (2013–2033). The projection results of our study can be of value to policy makers, such as the intergovernmental organisations related to environmental studies, e.g., the intergovernmental panel on climate change (IPCC). Inderscience Enterprises Ltd 2016-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/49995/1/IJGW090201_ABUBAKAR.pdf application/pdf en http://irep.iium.edu.my/49995/4/49995_Utilising%20key%20climate%20element%20variability%20for%20the%20prediction%20of%20future%20climate_Scopus.pdf Abubakar, Adamu and Haruna, Chiroma and Zeki, Akram M. and Uddin, Mueen (2016) Utilising key climate element variability for the prediction of future climate change using a support vector machine model. International Journal of Global Warming, 9 (2). pp. 129-151. ISSN 1758-209 (O), 1758-2083 (P) http://www.inderscienceonline.com/doi/abs/10.1504/IJGW.2016.074952 DOI: 10.1504/IJGW.2016.074952
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q350 Information theory
spellingShingle	Q350 Information theory Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Uddin, Mueen Utilising key climate element variability for the prediction of future climate change using a support vector machine model
description	This paper proposes a support vector machine (SVM) model to advance the prediction accuracy of global land-ocean temperature (GLOT), which is globally significant for understanding the future pattern of climate change. The GLOT dataset was collected from NASA’s GLOT index (C) (anomaly with base: 1951–1980) for the period 1880 to 2013. We categorise the dataset by decades to describe the behaviour of the GLOT within those decades. The dataset was used to build an SVM Model to predict future values of the GLOT. The performance of the model was compared with a multilayer perceptron neural network (MLPNN) and validated statistically. The SVM was found to perform significantly better than the MLPNN in terms of mean square error and root mean square error, although computational times for the two models are statistically equal. The SVM model was used to project the GLOT from the pre-existing NASA’s GLOT index (C) (anomaly with base: 1951–1980) for the next 20 years (2013–2033). The projection results of our study can be of value to policy makers, such as the intergovernmental organisations related to environmental studies, e.g., the intergovernmental panel on climate change (IPCC).
format	Article
author	Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Uddin, Mueen
author_facet	Abubakar, Adamu Haruna, Chiroma Zeki, Akram M. Uddin, Mueen
author_sort	Abubakar, Adamu
title	Utilising key climate element variability for the prediction of future climate change using a support vector machine model
title_short	Utilising key climate element variability for the prediction of future climate change using a support vector machine model
title_full	Utilising key climate element variability for the prediction of future climate change using a support vector machine model
title_fullStr	Utilising key climate element variability for the prediction of future climate change using a support vector machine model
title_full_unstemmed	Utilising key climate element variability for the prediction of future climate change using a support vector machine model
title_sort	utilising key climate element variability for the prediction of future climate change using a support vector machine model
publisher	Inderscience Enterprises Ltd
publishDate	2016
url	http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/ http://irep.iium.edu.my/49995/1/IJGW090201_ABUBAKAR.pdf http://irep.iium.edu.my/49995/4/49995_Utilising%20key%20climate%20element%20variability%20for%20the%20prediction%20of%20future%20climate_Scopus.pdf
first_indexed	2023-09-18T21:10:39Z
last_indexed	2023-09-18T21:10:39Z
_version_	1777411229659168768

Utilising key climate element variability for the prediction of future climate change using a support vector machine model

Similar Items