Gaining Insights Into Risks to Groundwater Using Big Data and Machine Learning
Monday, December 4, 2017: 11:30 a.m.
102 B (Music City Center)
Efforts to improve insights related to the risks to groundwater coinciding with oil and gas development and infrastructure requires an understanding of the dynamics and interactions across the entire engineered-natural system. However, the heterogeneity and ambiguity of these data make it difficult to assess the broad range of potential risks posed to groundwater. But, successful applications of big data and machine learning in other scientific disciplines suggest these approaches are well suited to coping with these types of challenges faced when assessing risks to groundwater. Here, we will present some of our efforts and lessons learned integrating machine learning and big data analytics in various models and assessments to understand risk associated with oil and gas development and infrastructure as it pertains to groundwater.