H094:
Prediction of Hydrologic Behavior from Sparse Information Using Statistical and Machine Learning Techniques





Session ID#: 27468

Session Description:
Recent years have seen the development of a variety of techniques for modeling flow and transport behavior which exhibit better fidelity to observational data than classical models. One limitation of such techniques has been the difficulty of using them in a predictive context by constraining their parameters a priori. The recent flourishing of development in machine learning techniques and high-performance computing promises change this situation, and allow for the discovery of predictive relationships relating hydrologic observables to quantities of interest. Such techniques also make it possible to derive empirically-grounded error envelopes for predictions. With this motivation, the session aims to highlight recent theoretical and technical advances in predictive flow and (reactive) transport modeling, as well as error analysis, throughout the hydrologic sciences. These include advances may include approaches employing innovative machine learning techniques, as well as those employing classic statistical regression, Bayesian, and other approaches.
Primary Convener:  Scott K Hansen, Los Alamos National Laboratory, Computational Earth Sciences (EES-16), Los Alamos, NM, United States
Conveners:  Yashar Mehmani, Stanford University, Energy Resources Engineering, Stanford, CA, United States and Maruti Mudunuru, Los Alamos National Laboratory, Los Alamos, NM, United States

Abstracts Submitted to this Session:

Sonja Molnos, Potsdam Institute for Climate Impact Research, Potsdam, Germany
Claus P Haslauer, University of Tübingen, Stuttgart, Germany and András Bárdossy, University of Stuttgart, Department of Hydrology and Geohydrology, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart, Germany
Katherine H. Markovich1, Jose Luis Arumi2, Helen E Dahlke1 and Graham E Fogg3, (1)University of California Davis, Davis, CA, United States, (2)Universidad de Concepcion, Chillan, Chile, (3)Univ California Davis, Davis, CA, United States
Mohammadhossein Alipour and Kelly Maren Kibler, University of Central Florida, Orlando, FL, United States
Justin Montgomery and Francis O'sullivan, Massachusetts Institute of Technology, Cambridge, MA, United States
Stephen P Good, Oregon State University, Biological and Ecological Engineering, Corvallis, OR, United States, Dawn R. URycki, Oregon State University, Biological & Ecological Engineering, Corvallis, OR, United States and Byron C Crump, Oregon State University, College of Earth, Ocean, and Atmospheric Science, Corvallis, OR, United States
Karina Cucchi1, Nura Kawa1, Falk Hesse2 and Yoram Rubin3, (1)University of California Berkeley, Berkeley, CA, United States, (2)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (3)Univ California Berkeley, Berkeley, CA, United States
Masoud Arshadi1, Linda M Abriola1, Eric L Miller1 and Clara De Paolis Kaluza2, (1)Tufts University, Medford, MA, United States, (2)Northeastern University, Boston, MA, United States
Elise E Wright1, Scott K Hansen2, Diogo Bolster1, David H Richter1 and Velimir V Vesselinov2, (1)University of Notre Dame, Notre Dame, IN, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States

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