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:...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Inderscience Enterprises Ltd
2016
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| Subjects: | |
| 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 |
| Summary: | 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). |
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