PROSPECTS FOR SHORT-TERM FORECASTING OF RIVER STREAMFLOW FROM SMALL WATERSHED RUNOFF USING MACHINE LEARNING METHODS
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, a 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 – river Protva (Spas-Zagorie gauge). Current and lagged values of streamflow discharge at the gauge and daily precipitation at local weather stations are used as predictors for the model, as well as moisture index and evaporation rate. 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. In contrast to the linear model, which efficiency decreases after lead time of more than 3 days, the artificial neural networks models have higher forecast efficiency up to 7 days. The results obtained are robust for all phases of the water regime, both spring floods and summer floods. The software implementation of the models is made on the basis of open software libraries in the Python language, which makes it possible to widely use the methods for scientific research and applied problems.
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