T246 Machine-Learning and Artificial-Intelligence Applications in the Geosciences (Posters)

Wednesday, 7 November 2018
9:00 AM - 6:30 PM
Halls J-K

DATA-DRIVEN APPROACH TO OBSERVING THE ELUSIVE ERODIBILITY COEFFICIENT

Paper number: T246-1
Program: Technical Sessions
Time: 9:00 AM – 6:30 PM

Authors

Abstract

The development of computer-based fluvial landscape evolution models has provided copious insights into how drainage systems respond to tectonics, sediment load, climate change, and landscape. Relatively simple, yet powerful equations are used in these models to predict stream bedrock incision. However, these equations come at a cost: the introduction of parameters not grounded in field observations. Most notably, the erodibility coefficient is often used. Here we present a data-driven approach to estimate the erodibility of a landscape. We show that we can predict erodibility from the landsurface in landscape evolution models. We also are testing this method on natural landscapes.

Geological Society of America Abstracts with Programs. Vol. 50, No. 6

doi: 10.1130/abs/2018AM-319689

© Copyright 2018 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.