Prospects for short-term forecasting of river streamflow runoff using machine learning methods – a case study of the Protva river

Methods, models and technologies
  • Всеволод Морейдо Институт водных проблем РАН
Keywords: hydrological forecasts, machine learning, river flow forecasts, artificial neural networks, MLP, LSTM
(+) Abstract

The paper addresses prospects for short-term (from 1 to 7 days) forecasting of river streamflow runoff based on several machine learning methods: multiple linear regression (LM) model, an multilayer perceptron (MLP) artificial neural network, and a recurrent artificial neural network with long short-term memory (LSTM). Methods for expanding the set of predictors for model construction are proposed, and the possibility of random shuffling of the time-series of predictors for model calibration and verification are assessed. The object of the study is the small river of Central Russia - r. Protva (Spas-Zagorie gauge). The obtained results show the possibility of constructing an effective operational forecasting system for short-term runoff forecasting. The study revealed the applicability of artificial neural network models, acceptable for operational practice, using all available hydrometeorological information on the catchment, as they showed the most stable results at all lead times from 1 to 7 days.

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How to Cite
Морейдо, В. (2022). Prospects for short-term forecasting of river streamflow runoff using machine learning methods – a case study of the Protva river. Hydrosphere. Hazard Processes and Phenomena, 2(4). Retrieved from


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