气候变化领域集成服务门户(CCPortal): Machine learning in geo- and environmental sciences: From small to large scale

CCPortal

DOI	10.1016/j.advwatres.2020.103619
	Machine learning in geo- and environmental sciences: From small to large scale
	Tahmasebi P.; Kamrava S.; Bai T.; Sahimi M.
发表日期	2020
ISSN	0309-1708
卷号	142
英文摘要	In recent years significant breakthroughs in exploring big data, recognition of complex patterns, and predicting intricate variables have been made. One efficient way of analyzing big data, recognizing complex patterns, and extracting trends is through machine-learning (ML) algorithms. The field of porous media, and more generally geoscience, have also witnessed much progress, and recent progress in developing various ML techniques have benefitted various problems in porous media and geoscience across disparate scales. Thus, it is becoming increasingly clear that it is imperative to adopt advanced ML methods for the problems in porous media and geoscience because they enable researchers to solve many difficult problems. At the same time, one can use the already existing extensive knowledge of porous media to endow ML algorithms and develop novel physics-guided methods. The goal of this review paper is to provide the first comprehensive review of the recently developed methods in the ML algorithms and describe their application to porous media and geoscience. Thus, we review the basic concept of the ML and describe more advanced methods, known as deep-learning algorithms. Then, the application of such methods to various problems in porous media and geoscience, such as hydrological modeling, fluid flow in porous media, and (sub)surface characterization, are reviewed. We also provide a discussion of future directions in this rapidly developing field. © 2020
关键词	Big data Deep learning Flow of fluids Geology Learning systems Pattern recognition Porous materials Basic concepts Complex pattern Environmental science Fluid flow in porous media Hydrological modeling Ml algorithms Recent progress Surface characterization Learning algorithms
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131756
推荐引用方式 GB/T 7714	Tahmasebi P.,Kamrava S.,Bai T.,et al. Machine learning in geo- and environmental sciences: From small to large scale[J]. Advances in Water Resources,2020,142.
APA	Tahmasebi P.,Kamrava S.,Bai T.,&Sahimi M..(2020).Machine learning in geo- and environmental sciences: From small to large scale.,142.
MLA	Tahmasebi P.,et al."Machine learning in geo- and environmental sciences: From small to large scale".142(2020).