Regression-based machine learning approaches for daily streamflow modeling

Samadi, Vidya S., Tabas, Sadgeh Sadeghi, Wilson, Catherine A. M. E. ORCID: https://orcid.org/0000-0002-7128-590X and Hitchcock, Daniel R. 2024. Regression-based machine learning approaches for daily streamflow modeling. Corzo Perez, G. and Solomatine, D. P., eds. Advanced Hydroinformatics: Machine Learning and Optimization for Water Resources, John Wiley & Sons, pp. 129-147. (10.1002/9781119639268.ch5)

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Abstract

The increasing rise of computational power has led to significant advances in the application of machine learning (ML) techniques in hydrological simulation. These methods are specifically developed to discover constitutive relations by implementing an unbiased implicit approach to capture unforeseen patterns in massive data sets. This study applied multiple ML approaches for daily rainfall-runoff simulation across a mixed urban-rural drainage system, the Northeast Cape Fear River Basin in North Carolina (NC), USA. Multiple ML algorithms such as Support Vector Machines (SVM), Bayesian Lasso, and Random Forest (RF) models, along with the Sacramento Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, were applied to predict sequential daily streamflow records based on a set of collected data from climate and streamflow gauging stations. Analysis suggests that the effects of input data on model performance, the error associated with forcing data, the amount of training data, and the correlation among different attributes of data series have strong influences on ML computation. Compared to the SAC-SMA, the Bayesian Lasso model was able to simulate the temporal dependencies among the observations and thereby was capable of accurately modeling the multivariate sequences of complex daily rainfall-runoff records across a mixed urban-rural catchment. The results provided an algorithmically informed simulation on the dynamics of daily streamflow simulation that may apply to other complex catchments and climate settings.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	John Wiley & Sons
ISBN:	9781119639312
ISSN:	2328-9279
Last Modified:	12 Feb 2024 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/165535

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