Dataset Information

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 functions. To evaluate the performance of FSO, we conducted a case study using a parsimonious hydrological model and synthetic discharge data. The case study consisted of two FSO applications: single-criteria FSO, where only discharge was used for optimization, and multicriteria FSO, where additional spatiotemporal observations of model states were used for transfer function estimation. The results show that FSO is able to estimate transfer functions correctly or approximate them sufficiently. We observed a reduced fit of the parameter density functions resulting from the inferred transfer functions for less sensitive model parameters. For those it was sufficient to estimate functions resulting in parameter distributions with approximately the same mean parameter values as the real transfer functions. The results of the multicriteria FSO showed that using multiple spatiotemporal observations for optimization increased the quality of estimation considerably.

SUBMITTER: Feigl M

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

REPOSITORIES: biostudies-literature

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

Feigl M M Herrnegger M M Klotz D D Schulz K K

Water resources research 20201006 10

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 ...[more]

PMID: 33132450

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Project description:Since the current clamp-type and push-out-type seedling picking method brought damage to seedlings, this study aimed to proposed an airflow ejection-wrapped clamping type seedling picking method, which used airflow to eject out seedling and the seedlings were wrapped clamped to reduce the damage of seedlings during seedling picking process. The parameter model was established through theoretical design, then the parameters were optimized through coupling simulation analysis, and the validity of these parameters was verified through experiments. We found that the diameter of the airflow nozzle was selected as 3.5 mm to match with the drainage outlet of the plug tray, and the airflow pressure which could eject out seedlings was calculated as 0.146 Mpa~0.315 Mpa on the basis of gas jet dynamic. The fluid-solid coupling simulation of airflow ejection in Comsol proposed that the seedlings could be ejected out under the airflow pressure was equal to or greater than 0.4 Mpa, and the airflow should be maintained for about 0.3 s to ensure the posture of the seedlings ejected out for better seedling clamping. The further fluid-discrete body simulation of airflow ejection by using Fluent-Edem coupling method indicated that the seedling was damaged under airflow pressure of 0.5 MPa, so the airflow pressure should be set as 0.4 MPa during seedling ejection process. Besides, a wrapped clamping type effector which clamped the seedlings from all sides in the form of flexible package was also designed to match with the airflow ejection method, and the RecurDyn-Edem coupling simulation showed that the end-effector could tightly clamp the seedling without damage when the angle between the clamping slices and the vertical direction was 8.5°. Finally, the airflow ejection-wrapped clamping type seedling picking device was manufactured, and the verification tests verified the simulation results. This research can provide some references for the automatic seedling picking technology.

Dataset Information

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

Publications

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

Similar Datasets

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