A new U.S. Geological Survey report outlines how emerging technologies and cross-disciplinary collaborations are expected to empower new tools for managing hazards and resources.
Leaders at the U.S. Geological Survey (USGS) expect the proliferation of networked devices, inexpensive sensors, and drones to make an explosion of massive data sets available to Earth scientists. At the same time, advances in cloud computing and artificial intelligence will enable more powerful models for understanding these data and using them to project into the future. This is the outlook from the USGS 21st-Century Science Strategy 2020–2030. The report, released in January, describes USGS’s growth from its foundation in traditional observational science to a resource for predictive tools that can guide decision-makers in the management of natural resources and environmental hazards.
Experts have said that realizing this vision will require communication across disciplines and support for scientists who engage in interdisciplinary work. “In order to anticipate things that might happen in the short term and in the long term, we need to start looking at the Earth as a system of systems,” said Geoffrey Plumlee, chief scientist at USGS. Originally trained as a geologist, Plumlee has spent many years looking at the intersections between geology, environmental disasters, and human health.
Where others see geophysics problems, biomedical problems, and climate science problems, Karianne Bergen sees one problem: a data problem. Scientists go out into their respective fields and collect mounds of data. Some sets of parameters, combined in the right way, form a model that explains not only the observations but also countless other possible observations. That’s the data problem. Solving the data problem provides an insight into the future.
“Advances related to computing and data science can translate from one discipline to another,” said Bergen. “If someone finds a good strategy that works for geophysics, someone in climate science may be able to adopt it.” As an assistant professor of Earth, environmental, and planetary sciences and data science at Brown University, Bergen uses machine learning to look for these solutions.
Machine learning is a branch of artificial intelligence that uses optimization to create models based on existing data, which can then be used to make predictions. Instead of giving a computer an equation and asking it to solve for a solution, scientists give the computer a set of results and ask it to find the best equation. When applied to Earth systems, these models can be used to anticipate the effects of policy decisions and future changes in the environment.
Author: Matthew Stonecash