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:	Sara Ibrahim, Ibrahim Ziedan, Ayman Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Climate change greenhouse gases (GHGs) machine learning (ML) neural network space systems Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/321dd4f8adb74ec7ae69c48ecfbf3f93
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id	oai:doaj.org-article:321dd4f8adb74ec7ae69c48ecfbf3f93
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spelling	oai:doaj.org-article:321dd4f8adb74ec7ae69c48ecfbf3f932021-11-18T00:00:22ZStudy of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data2151-153510.1109/JSTARS.2021.3120987https://doaj.org/article/321dd4f8adb74ec7ae69c48ecfbf3f932021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9580649/https://doaj.org/toc/2151-1535The 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.Sara IbrahimIbrahim ZiedanAyman AhmedIEEEarticleClimate changegreenhouse gases (GHGs)machine learning (ML)neural networkspace systemsOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11080-11094 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Climate change greenhouse gases (GHGs) machine learning (ML) neural network space systems Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Climate change greenhouse gases (GHGs) machine learning (ML) neural network space systems Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Sara Ibrahim Ibrahim Ziedan Ayman Ahmed Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
description	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.
format	article
author	Sara Ibrahim Ibrahim Ziedan Ayman Ahmed
author_facet	Sara Ibrahim Ibrahim Ziedan Ayman Ahmed
author_sort	Sara Ibrahim
title	Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
title_short	Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
title_full	Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
title_fullStr	Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
title_full_unstemmed	Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data
title_sort	study of climate change detection in north-east africa using machine learning and satellite data
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/321dd4f8adb74ec7ae69c48ecfbf3f93
work_keys_str_mv	AT saraibrahim studyofclimatechangedetectioninnortheastafricausingmachinelearningandsatellitedata AT ibrahimziedan studyofclimatechangedetectioninnortheastafricausingmachinelearningandsatellitedata AT aymanahmed studyofclimatechangedetectioninnortheastafricausingmachinelearningandsatellitedata
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Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data

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