干旱区生态环境知识资源中心(Arid): Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach

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DOI	10.1007/s12145-021-00744-w
	Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach
	El Alaoui El Fels, Abdelhafid; El Ghorfi, Mustapha
通讯作者	El Fels, AE (corresponding author)，Univ Cadi Ayyad, Fac Sci & Tech, Dept Geol, Lab Geosci & Environm LGSE, BP 549, Marrakech, Morocco.
来源期刊	EARTH SCIENCE INFORMATICS
ISSN	1865-0473
EISSN	1865-0481
出版年	2022
卷号	15 期号:1 页码:485-496
英文摘要	The geological map encapsulates basic information that can be crucial in a multitude of fields such as landslide risk assessment, engineering projects, as well as petroleum and mineral resources studies. In addition, it is difficult, expensive and time-consuming to achieve it in complex and inaccessible lands. However, remote sensing data linking and the application of Machine Learning Algorithms (MLAs) can be interesting for geological mapping of large areas, especially in arid and semi-arid regions, where remote sensing provides a diversified and detailed spatial database and MLAs offer the possibility of effective and efficient classification of remotely sensed images. This article highlights the use of Aster spectral data in a comparative approach of the performance of six (MLAs) to better produce the geological map of a portion of the Ait Ahmane region. The results indicated an overall Accuracy and a kappa coefficient that exceeded 60% for the different models. Prioritizing the Regularized Discriminant Analysis (RDA) (Kappa = 83.5%) and Support Vector Machines (SVM) (Kappa = 81%) algorithms, they managed to classify the lithology on Aster images of the region. However, the classification of lithology using the RDA was slightly more accurate than the one obtained by SVM with 2.3%. From the results shown, we can conclude that the ability of RDA as a learning algorithm is the best for the geological mapping of our study site.
英文关键词	Aster imagery Lithological classification Machine learning Semi-arid
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000749967400002
WOS关键词	LAND-COVER ; ANTI-ATLAS ; GEOCHRONOLOGICAL CONSTRAINTS ; CLASSIFICATION ; ASTER ; OPHIOLITE ; PERFORMANCE ; MAGMATISM ; ACCURACY ; COMPLEX
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS研究方向	Computer Science ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376336
作者单位	[El Alaoui El Fels, Abdelhafid; El Ghorfi, Mustapha] Univ Cadi Ayyad, Fac Sci & Tech, Dept Geol, Lab Geosci & Environm LGSE, BP 549, Marrakech, Morocco; [El Ghorfi, Mustapha] Mohammed VI Polytech Univ, Min Environm & Circular Econ EMEC, Lot 660, Hay Moulay Rachid 43150, Ben Guerir, Morocco
推荐引用方式 GB/T 7714	El Alaoui El Fels, Abdelhafid,El Ghorfi, Mustapha. Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach[J],2022,15(1):485-496.
APA	El Alaoui El Fels, Abdelhafid,&El Ghorfi, Mustapha.(2022).Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach.EARTH SCIENCE INFORMATICS,15(1),485-496.
MLA	El Alaoui El Fels, Abdelhafid,et al."Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach".EARTH SCIENCE INFORMATICS 15.1(2022):485-496.