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:...
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Inderscience Enterprises Ltd
2016
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| Online Access: | 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 http://irep.iium.edu.my/49995/ http://www.inderscienceonline.com/doi/abs/10.1504/IJGW.2016.074952 |
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my.iium.irep.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 REM 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 |
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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/49995/1/IJGW090201_ABUBAKAR.pdf http://irep.iium.edu.my/49995/4/49995_Utilising%20key%20climate%20element%20variability%20for%20the%20prediction%20of%20future%20climate_Scopus.pdf http://irep.iium.edu.my/49995/ http://www.inderscienceonline.com/doi/abs/10.1504/IJGW.2016.074952 |
| _version_ |
1643613640889204736 |
| score |
13.211869 |