Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models.

ABSTRACT: Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geophysical properties of a catchment, might improve the calibration procedure, increase process realism, and can enable prediction in ungauged areas. We present the function space optimization (FSO), a symbolic regression method for estimating parameter transfer functions for distributed hydrological models. FSO is based on the idea of transferring the search for mathematical expressions into a continuous vector space that can be used for optimization. This is accomplished by using a text generating neural network with a variational autoencoder architecture that can learn to compress the information of mathematical fu

SUBMITTER: Feigl M 

PROVIDER: S-EPMC7583385 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature