A new approach to river flow forecasting: LSTM and GRU multivariate models

Authors

  • Paulo Marcelo Tasinaffo Instituto Tecnológico de Aeronáutica (ITA)

Keywords:

artificial neural networks, long short term memory, river flow, time series forecasting, gated recurrent unit

Abstract

Hydroelectric power stations are responsible for renewable energy generation, especially in countries with many rivers as in Brazil. It is very important to have good estimates of the hydrological flow in order to determine if thermoelectric power plants should begin operation, an event that increases the costs of electricity and also has a terrible environmental impact. The monthly flow of a river was estimated using two recurrent neural networks techniques: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results were compared with other articles that had the same structure using the same data: the Rio Grande river in the Furnas and Camargos dam.

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Published

2020-02-16

How to Cite

Tasinaffo, P. M. (2020). A new approach to river flow forecasting: LSTM and GRU multivariate models. IEEE Latin America Transactions, 17(12), 1978–1986. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/2224

Issue

Section

Special Isssue on Deep Learning