Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
The study of climate change has become an important topic because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GH...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/321dd4f8adb74ec7ae69c48ecfbf3f93 |
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| Sumario: | The study of climate change has become an important topic because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GHGs) and climate change is an important factor to understand. To investigate this linkage, we used machine-learning (ML) models based on essential climate variables (ECVs) to investigate the relationship between the GHGs and the rhythm of climate variable change. The article investigates how ML techniques can be applied to climatic data to build an ML model that is able to predict the state of climate variables for the short and long term. By selecting a candidate model, it will help in climate adaptation and mitigation, also determine at what level GHGs should be kept and their corresponding concentrations in order to avoid climate events and crises. The used models are long short-term memory, autoencoders, and convolutional neural network (CNN). Alternatively, the dataset has been selected from U.K. National Centre for Earth Observation and Copernicus Climate Change Services. We compared the performance of these techniques and the best candidate was the Head—CNN; based on performance metrics such as root-mean-squared-error: 5.378, 2.395, and 15.923, mean-absolute-error: 4.157, 1.928, and11.672, Pearson: 0.368, 0.649, and 0.291, and <italic>R</italic><sup>2</sup> coefficient: 0.607, 0.806, and 0.539 for the ECVs temperature, CO<sub>2</sub>, and CH<sub>4</sub>, respectively. We were able to link the GHG emission to ECVs with high accuracy based on the reading of this geographic area. |
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