Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

no code implementations • 23 Oct 2023 • Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran, Zhen Liu, John T. Reager, Andrew M. Stuart, Michael J. Turmon

Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampled across time and space.

Gaussian Processes regression +1

Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

no code implementations • 8 Oct 2013 • Ciro Donalek, Arun Kumar A., S. G. Djorgovski, Ashish A. Mahabal, Matthew J. Graham, Thomas J. Fuchs, Michael J. Turmon, N. Sajeeth Philip, Michael Ting-Chang Yang, Giuseppe Longo

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible.

Astronomy feature selection +2