  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.

Signal Processing;Machine Learning;Applications;Machine Learning