In:
IMA Journal of Applied Mathematics, Oxford University Press (OUP), Vol. 89, No. 1 ( 2024-06-21), p. 123-142
Abstract:
We investigate the problem of learning a water quality model (BOD-DO model) from given data. Assuming that all parameters in the model are constants, we reformulate the problem as a system of linear equations for the unknown terms. Since in practice the system is often under-determined or over-determined and the observed data are noisy, we use an $l^{1}$-weighted regularization method to find a stable approximate solution. Then, Nesterov’s algorithm is used to solve the regularized problem. Learning models with variable coefficients are also discussed. Numerical examples show that our approach works well with noisy data and has the ability to learn the BOD-DO model.
Type of Medium:
Online Resource
ISSN:
0272-4960
,
1464-3634
DOI:
10.1093/imamat/hxad023
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2024
detail.hit.zdb_id:
283286-0
detail.hit.zdb_id:
1466709-5
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