Special issue on Machine Learning for Water Resources and Subsurface Systems
• 大类 : 地学 - 2区
• 小类 : 水资源 - 2区
Due to the enormous advances in computational power, machine-learning algorithms have recently experienced significant breakthroughs in handling and processing complex and big data. Water resources problems have always been among the issues that researchers have attempted to leverage such advances for various purposes, such as hydrological modeling, fluid flow in porous media, and (sub)surface characterization. As such, there is an overwhelming interest in developing machine-learning techniques for taking advantage of the big data that are becoming increasingly available from various sources, such as satellite images, sensors, drones, geophysical data, pore-scale imaging, etc., and discovering the relationships between important variables. In this special issue, we invite researchers to submit their papers on the use of machine-learning algorithms together with big data for addressing critical issues in water resources and similar (sub)surface problems. We are interested in papers that are related to novel applications of machine learning for solving the problems that either have never been explored in water resources, or those that require a considerable computational power using the traditional methods. Furthermore, we encourage papers on the combination of physics-based modeling of subsurface systems and machine learning. The final findings should either enrich our understating of important phenomena in water in subsurface systems, or present a novel view on the applications of machine learning and data-driven techniques for the aforementioned problems. Papers on the use of the traditional-machine learning algorithms using small datasets will not be considered in this special issue.
We encourage the authors to keep the focus on the problems related to water and subsurface systems. Authors are invited to submit their abstract to Pejman Tahmasebi and Muhammad Sahimi in PDF format. e‐mail: firstname.lastname@example.org, or email@example.com