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

M Feigl1, M Herrnegger1, D Klotz1,2

  • 1Institute for Hydrology and Water Management University of Natural Resources and Life Sciences Vienna Austria.

Water Resources Research
|November 2, 2020
PubMed
Summary
This summary is machine-generated.

Function space optimization (FSO) improves hydrological model parameter estimation. This symbolic regression method, using neural networks, effectively infers parameter transfer functions, enhancing predictions for ungauged areas.

Keywords:
distributed modelsmachine learningoptimizationrainfall‐runoff modelingregionalizationtransfer functions

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Area of Science:

  • Hydrology
  • Computational Science
  • Machine Learning

Background:

  • Estimating parameters for distributed hydrological models is complex.
  • Parameter transfer functions can improve model calibration and enable predictions in ungauged basins.
  • Existing methods may lack efficiency and process realism.

Purpose of the Study:

  • To introduce Function Space Optimization (FSO), a novel symbolic regression method for estimating parameter transfer functions.
  • To evaluate FSO's performance in estimating hydrological model parameter transfer functions using synthetic data.
  • To compare single-criteria and multicriteria FSO applications.

Main Methods:

  • Function Space Optimization (FSO) utilizes a text-generating neural network with a variational autoencoder architecture.
  • Mathematical expressions for transfer functions are searched in a continuous vector space.
  • Case study involved a parsimonious hydrological model and synthetic discharge data.
  • Single-criteria FSO used discharge data; multicriteria FSO incorporated additional spatiotemporal model state observations.

Main Results:

  • FSO successfully estimated or sufficiently approximated parameter transfer functions.
  • For less sensitive parameters, FSO produced functions with similar mean values to the true functions.
  • Multicriteria FSO, using multiple observations, significantly improved estimation quality.

Conclusions:

  • FSO is a viable method for estimating parameter transfer functions in distributed hydrological models.
  • The approach enhances model calibration and can extend predictive capabilities.
  • Incorporating multiple data sources in FSO substantially boosts estimation accuracy.