A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting

Malihe Danesh, Amin Gharehbaghi, Saeid Mehdizadeh () and Amirhossein Danesh
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Malihe Danesh: University of Science and Technology of Mazandaran
Amin Gharehbaghi: Hasan Kalyoncu University
Saeid Mehdizadeh: Urmia University
Amirhossein Danesh: Sungkyunkwan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 4, No 21, 1930 pages

Abstract: Abstract Forecasting river streamflow is crucial for hydrological science and optimal water resources management. In this study, six predictive methods were developed, including three machine learning models—random forest (RF), decision tree (DT), and K-nearest neighbors (KNN)—and three deep learning frameworks comprising convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid CNN-LSTM model. Two gauging stations on the McKenzie River in the United States (USGS 14162500 and USGS 14163900) were selected as case studies for model performance evaluation. Error metrics including root mean square error (RMSE), mean absolute error (MAE), determination coefficient (R²), and Kling-Gupta efficiency (KGE) were applied. Results demonstrated that the deep learning models consistently outperformed the machine learning methods for river streamflow forecasting at both sites. The hybrid CNN-LSTM model yielded the most accurate predictions. Specifically, the error metrics for the superior CNN-LSTM model during testing stage were as follows: at USGS 14162500, RMSE = 14.68 m³/s, MAE = 6.29 m³/s, R² = 0.930, and KGE = 0.960; at USGS 14163900, RMSE = 22.54 m³/s, MAE = 8.48 m³/s, R² = 0.882, and KGE = 0.935.

Keywords: Machine Learning; Deep Learning; River Streamflow; Forecasting; Standalone and Hybrid Models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-04052-y

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