2019/08/29 by Guanjie Zheng, Mengqi Liu, Zheng, Guanjie +11
Computer Science · Engineering · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.11056
8 pages, 4 figures, 1 table
arxiv created 2019/08/29 · arxiv updated 2019/08/30
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, wastewater transport, and urbanization are included. In addition to the occurrence of anthropogenic contamination, the presence of some contaminants (e.g., methane, salt, and sulfate) of natural origin is not uncommon. Therefore, scientists sometimes find it difficult to identify the sources of contaminants in the coupled natural and human systems. In this paper, we propose a technique to simultaneously conduct source detection and prediction, which outperforms other approaches in the interdisciplinary case study of the identification of potential groundwater contamination within a region of high-density shale gas development.