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Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting

  • Research Article - Hydrology
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Abstract

This article explores the suitability of a long short-term memory recurrent neural network (LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The long short-term memory works on the sequential framework which considers all of the predecessor data. This forecasting method used daily discharged data collected from the Basantapur gauging station located on the Mahanadi River basin, India. Different metrics [root-mean-square error (RMSE), Nash–Sutcliffe efficiency (ENS), correlation coefficient (R) and mean absolute error] were selected to assess the performance of the model. Additionally, recurrent neural network (RNN) model is also used to compare the adaptability of LSTM-RNN over RNN and naïve method. The results conclude that the LSTM-RNN model (R = 0.943, ENS = 0.878, RMSE = 0.487) outperformed RNN model (R = 0.935, ENS = 0.843, RMSE = 0.516) and naïve method (R = 0.866, ENS = 0.704, RMSE = 0.793). The finding of this research concludes that LSTM-RNN can be used as new reliable AI technique for low-flow forecasting.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Patna, India

    Bibhuti Bhusan Sahoo, Ramakar Jha, Anshuman Singh & Deepak Kumar

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  1. Bibhuti Bhusan Sahoo
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  2. Ramakar Jha
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  3. Anshuman Singh
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  4. Deepak Kumar
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Correspondence to Bibhuti Bhusan Sahoo.

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Sahoo, B.B., Jha, R., Singh, A. et al. Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophys. 67, 1471–1481 (2019). https://doi.org/10.1007/s11600-019-00330-1

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