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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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References

  • Abidogun OA (2005) Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks. University of the Western Cape, Cape Town

    Google Scholar 

  • Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4:e00938

    Article  Google Scholar 

  • Ahn KH, Palmer RN (2016) Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin. Hydrol Process 30:3518–3532

    Article  Google Scholar 

  • Arena C, Cannarozzo M, Mazzola MR (2006) Multi-year drought frequency analysis at multiple sites by operational hydrology–a comparison of methods. Phys Chem Earth Parts A/B/C 31:1146–1163

    Article  Google Scholar 

  • ASCE (2000a) Artificial neural networks in hydrology. I: Preliminary concepts. J Hydrol Eng 5:115–123

    Article  Google Scholar 

  • ASCE (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. J Hydrol Eng 5:124–137

    Article  Google Scholar 

  • Assaad M, Boné R, Cardot H (2008) A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inf Fusion 9:41–55

    Article  Google Scholar 

  • Atiya AF, El-Shoura SM, Shaheen SI, El-Sherif MS (1999) A comparison between neural-network forecasting techniques-case study: river flow forecasting. IEEE Trans Neural Netw 10:402–409

    Article  Google Scholar 

  • Bandara K, Bergmeir C, Smyl S (2017) Forecasting across time series databases using long short-term memory networks on groups of similar series. arXiv preprint arXiv:171003222

  • Beven KJ (2012) Rainfall-runoff modelling: the primer. Wiley, New York

    Book  Google Scholar 

  • Box G, Jenkins G (1970) Time series analysis; forecasting and control. Holden-Day, San Francisco

    Google Scholar 

  • Carlson RF, MacCormick A, Watts DG (1970) Application of linear random models to four annual streamflow series. Water Resour Res 6:1070–1078

    Article  Google Scholar 

  • Chang F, Chang LC, Huang HL (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrol Process 16:2577–2588

    Article  Google Scholar 

  • Chen H-L, Rao AR (2002) Testing hydrologic time series for stationarity. J Hydrol Eng 7:129–136

    Article  Google Scholar 

  • Cheng C, Chau K, Sun Y, Lin J (2005) Long-term prediction of discharges in Manwan Reservoir using artificial neural network models. Adv Neural Netw 2005:975

    Google Scholar 

  • Chollet F (2016) Keras. https://github.com/fchollet/keras/tree/master/keras

  • Cinar YG, Mirisaee H, Goswami P, Gaussier E, Aït-Bachir A, Strijov V (2017) Position-based content attention for time series forecasting with sequence-to-sequence RNNs. In: International conference on neural information processing. Springer, pp 533–544

  • Demirel MC, Booij MJ, Hoekstra AY (2013) Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times. Hydrol Process 27:2742–2758

    Article  Google Scholar 

  • Dimitriadis P, Koutsoyiannis D (2015) Climacogram versus autocovariance and power spectrum in stochastic modelling for Markovian and Hurst-Kolmogorov processes. Stoch Environ Res Risk Assess 29:1649–1669

    Article  Google Scholar 

  • Dimitriadis P, Koutsoyiannis D, Tzouka K (2016) Predictability in dice motion: how does it differ from hydro-meteorological processes? Hydrol Sci J 61:1611–1622

    Article  Google Scholar 

  • Dracup JA, Lee KS, Paulson EG (1980) On the definition of droughts. Water Resour Res 16:297–302

    Article  Google Scholar 

  • Fang K, Shen C, Kifer D, Yang X (2017) Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network. Geophys Res Lett 44:11–15

    Article  Google Scholar 

  • Firat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96

    Article  Google Scholar 

  • Firat M, Güngör M (2008) Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrol Process 22:2122–2132

    Article  Google Scholar 

  • Gárfias-Soliz J, Llanos-Acebo H, Martel R (2010) Time series and stochastic analyses to study the hydrodynamic characteristics of karstic aquifers. Hydrol Process 24:300–316

    Google Scholar 

  • Gers F (2001) Long short-term memory in recurrent neural networks Unpublished PhD dissertation. Ecole Polytechnique Fédérale de Lausanne, Lausanne

    Google Scholar 

  • Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. 850–855

  • Giuntoli I, Renard B, Vidal J-P, Bard A (2013) Low flows in France and their relationship to large-scale climate indices. J Hydrol 482:105–118

    Article  Google Scholar 

  • Gustard A, Demuth S (2009) Manual on low-flow estimation and prediction. Opera

  • Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems, vol 45. Elsevier, Amsterdam

    Book  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  • Hu T, Lam K, Ng S (2001) River flow time series prediction with a range-dependent neural network. Hydrol Sci J 46:729–745

    Article  Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts, Melbourne

    Google Scholar 

  • Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592

    Article  Google Scholar 

  • Jha R, Smakhtin V (2008) A review of methods of hydrological estimation at ungauged sites in India, vol 130. IWMI, Colombo

    Google Scholar 

  • Jha R, Sharma K, Singh V (2008) Critical appraisal of methods for the assessment of environmental flows and their application in two river systems of India. KSCE J Civ Eng 12:213–219

    Article  Google Scholar 

  • Keskin ME, Taylan D, Terzi O (2006) Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrol Sci J 51:588–598

    Article  Google Scholar 

  • Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12:532–539

    Article  Google Scholar 

  • Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140

    Article  Google Scholar 

  • Komorník J, Komorníková M, Mesiar R, Szökeová D, Szolgay J (2006) Comparison of forecasting performance of nonlinear models of hydrological time series. Phys Chemi Earth Parts A/B/C 31:1127–1145

    Article  Google Scholar 

  • Koutsoyiannis D, Langousis A (2011) Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78. Academic Press, Oxford

  • Koutsoyiannis D, Yao H, Georgakakos A (2008) Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods/Prévision du débit du Nil à moyen terme: une comparaison de méthodes stochastiques et déterministes. Hydrol Sci J 53:142–164

    Article  Google Scholar 

  • Laaha G, Blöschl G (2005) Low flow estimates from short stream flow records—a comparison of methods. J Hydrol 306:264–286

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436

    Article  Google Scholar 

  • Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599–612

    Article  Google Scholar 

  • Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. In: Interspeech, p 3

  • Nayak PC, Sudheer K, Rangan D, Ramasastri K (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52–66

    Article  Google Scholar 

  • Ouyang Q, Lu W (2018) Monthly rainfall forecasting using echo state networks coupled with data preprocessing methods. Water Resour Manag 32:659–674

    Article  Google Scholar 

  • Papacharalampous G, Tyralis H, Koutsoyiannis D (2018a) One-step ahead forecasting of geophysical processes within a purely statistical framework. Geosci Lett 5:12

    Article  Google Scholar 

  • Papacharalampous G, Tyralis H, Koutsoyiannis D (2018b) Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophys 66(4):807–831

    Article  Google Scholar 

  • Papacharalampous G, Tyralis H, Koutsoyiannis D (2018c) Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: a multiple-case study from Greece. Water Resour Manag 32:5207–5239

    Article  Google Scholar 

  • Papacharalampous GA, Tyralis H, Koutsoyiannis D (2019) Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-018-1638-6

    Google Scholar 

  • Pyrce R (2004) Hydrological low flow indices and their uses Watershed Science Centre, (WSC) Report

  • Sahoo S, Russo T, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resour Res 53(5):3878–3895

    Article  Google Scholar 

  • Sahoo BB, Jha R, Singh A, Kumar D (2018) Application of support vector regression for modeling low flow time series. KSCE J Civ Eng 23(2):923–934

    Article  Google Scholar 

  • Salas JD (1993) Analysis and modeling of hydrologic time series. In: Maidment DR (ed) Handbook of hydrology, vol 19. McGraw Hill, New York, pp 19.1–19.72

    Google Scholar 

  • Sang Y-F (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15

    Article  Google Scholar 

  • Sang Y-F, Wang D, Wu J-C, Zhu Q-P, Wang L (2009) The relation between periods’ identification and noises in hydrologic series data. J Hydrol 368:165–177

    Article  Google Scholar 

  • Schoups G, Van de Giesen N, Savenije H (2008) Model complexity control for hydrologic prediction. Water Resources Res 44:W00B03

    Article  Google Scholar 

  • Sivapragasam C, Liong S-Y, Pasha M (2001) Rainfall and runoff forecasting with SSA–SVM approach. J Hydroinform 3:141–152

    Article  Google Scholar 

  • Sivapragasam C, Vincent P, Vasudevan G (2007) Genetic programming model for forecast of short and noisy data. Hydrol Process 21:266–272

    Article  Google Scholar 

  • Smakhtin V (2001) Low flow hydrology: a review. J Hydrol 240:147–186

    Article  Google Scholar 

  • Srikanthan R, McMahon T (2001) Stochastic generation of annual, monthly and daily climate data: a review. Hydrol Earth Syst Sci Discuss 5:653–670

    Article  Google Scholar 

  • Tegos A, Schlüter W, Gibbons N, Katselis Y, Efstratiadis A (2018) Assessment of environmental flows from complexity to parsimony—lessons from Lesotho. Water 10:1293

    Article  Google Scholar 

  • Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147

    Article  Google Scholar 

  • Tyralis H, Koutsoyiannis D (2014) A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables. Clim Dyn 42:2867–2883

    Article  Google Scholar 

  • Tyralis H, Papacharalampous G (2017) Variable selection in time series forecasting using random forests. Algorithms 10:114

    Article  Google Scholar 

  • Tyralis H, Papacharalampous GA (2018) Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow. Adv Geosci 45:147–153

    Article  Google Scholar 

  • Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306

    Article  Google Scholar 

  • WMO (2008) Manual on low flow estimation and prediction. WMO, Geneva

    Google Scholar 

  • Wunsch A, Liesch T, Broda S (2018) Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). J Hydrol 567:743–758

    Article  Google Scholar 

  • Xu L, Chen N, Zhang X, Chen Z (2018) An evaluation of statistical, NMME and hybrid models for drought prediction in China. J Hydrol 566:235–249

    Article  Google Scholar 

  • Yaseen ZM, El-Shafie A, Jaafar O, Afan HA, Sayl KN (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844

    Article  Google Scholar 

  • Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614

    Article  Google Scholar 

  • Yaseen ZM, Fu M, Wang C, Mohtar WHMW, Deo RC, El-Shafie A (2018) Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons. Water Resour Manag 32(5):1883–1899

    Article  Google Scholar 

  • Zhang D, Lindholm G, Ratnaweera H (2018a) Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring. J Hydrol 556:409–418

    Article  Google Scholar 

  • Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018b) Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929

    Article  Google Scholar 

  • Zounemat-Kermani M, Teshnehlab M (2008) Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl Soft Comput 8:928–936

    Article  Google Scholar 

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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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