Improving Streamflow Forecasting Efficiency Using Signal Decomposition Approaches

Dinesh Kumar Vishwakarma (), Salim Heddam (), Arpit Gaur (), Ravindra Kumar Tiwari (), Ozgur Kisi (), Anurag Malik (), Chetak Bishnoi (), Abed Alataway (), Ahmed Z. Dewidar () and Mohamed A. Mattar ()
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Dinesh Kumar Vishwakarma: Govind Ballabh Pant University of Agriculture and Technology
Salim Heddam: Faculty of Science, Agronomy Department, Hydraulics Division
Arpit Gaur: Montana State University
Ravindra Kumar Tiwari: Rani Lakshmi Bai Central Agricultural University
Ozgur Kisi: Lübeck University of Applied Sciences
Anurag Malik: Regional Research Station
Chetak Bishnoi: Regional Research Station
Abed Alataway: King Saud University
Ahmed Z. Dewidar: King Saud University
Mohamed A. Mattar: King Saud University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 12, No 21, 6459-6492

Abstract: Abstract This study introduces a novel approach utilizing the Maximal Overlap Discrete Wavelet Transform (MODWT) to enhance daily streamflow forecasting at two USGS stations (14211500 and 14211550) from 1998 to 2021. The MODWT is integrated with three machine learning models: Extremely Randomized Trees (ERT), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR). Autocorrelation and partial autocorrelation functions were employed to determine relevant lags and generate multiple input variables, which were then analyzed through MODWT to derive multi-resolution analysis features. The hybrid model incorporating MODWT significantly improved prediction accuracy. Among the methods, ANN with MODWT (ANN6_MODWT) demonstrated superior performance compared to standalone ANN, ERT, and GPR models. ANN6_MODWT achieved improvements of 15.60%, 24.70%, 39.74%, and 28.34% in terms of correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) at USGS 14211550, and 13.50%, 23.80%, 46.47%, and 34.06% at USGS 14211500. These results underscore the potential of MODWT for enhancing streamflow prediction accuracy. Graphical Abstract

Keywords: Forecasting; Streamflow; Extremely randomized trees; Artificial neural network; Gaussian process regression; Maximum overlap discrete wavelet transform (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04258-8

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