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

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

doi:10.1007/s11600-019-00330-1

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Tytuł artykułu

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

Autorzy

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

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

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DOI

10.1007/s11600-019-00330-1

Warianty tytułu

Języki publikacji

Abstrakty

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. Diferent metrics [root-mean-square error (RMSE), Nash–Sutclife efciency (E_NS), correlation coefcient (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, E_NS=0.878, RMSE=0.487) outperformed RNN model (R=0.935, E_NS=0.843, RMSE=0.516) and naïve method (R=0.866, E_NS=0.704, RMSE=0.793). The fnding of this research concludes that LSTM-RNN can be used as new reliable AI technique for low-flow forecasting.

Słowa kluczowe

artificial intelligence long short-term memory recurrent neural network low flow hydrological time series forecasting naïve method

sztuczna inteligencja długotrwała sieć neuronowa z powtarzalną pamięcią przepływ niski prognozowanie hydrologicznych szeregów czasowych metoda naiwna

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2019

Tom

Vol. 67, no. 5

Strony

1471--1481

Opis fizyczny

Bibliogr. 78 poz.

Twórcy

autor

Sahoo Bibhuti Bhusan

bibhuti5000@gmail.com

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

autor

Jha Ramakar

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

autor

Singh Anshuman

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

autor

Kumar Deepak

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

Bibliografia

1. Abidogun OA (2005) Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks. University of the Western Cape, Cape Town
2. 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
3. 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
4. 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
5. ASCE (2000a) Artificial neural networks in hydrology. I: Preliminary concepts. J Hydrol Eng 5:115–123
6. ASCE (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. J Hydrol Eng 5:124–137
7. 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
8. 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
9. 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
10. Beven KJ (2012) Rainfall-runoff modelling: the primer. Wiley, New York
11. Box G, Jenkins G (1970) Time series analysis; forecasting and control. Holden-Day, San Francisco
12. Carlson RF, MacCormick A, Watts DG (1970) Application of linear random models to four annual streamflow series. Water Resour Res 6:1070–1078
13. Chang F, Chang LC, Huang HL (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrol Process 16:2577–2588
14. Chen H-L, Rao AR (2002) Testing hydrologic time series for stationarity. J Hydrol Eng 7:129–136
15. 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
16. Chollet F (2016) Keras. https://github.com/fchollet/keras/tree/master/keras
17. 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
18. 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
19. 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
20. 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
21. Dracup JA, Lee KS, Paulson EG (1980) On the definition of droughts. Water Resour Res 16:297–302
22. 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
23. Firat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96
24. Firat M, Güngör M (2008) Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrol Process 22:2122–2132
25. 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
26. Gers F (2001) Long short-term memory in recurrent neural networks Unpublished PhD dissertation. Ecole Polytechnique Fédérale de Lausanne, Lausanne
27. Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. 850–855
28. 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
29. Gustard A, Demuth S (2009) Manual on low-flow estimation and prediction. Opera
30. Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems, vol 45. Elsevier, Amsterdam
31. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780
32. Hu T, Lam K, Ng S (2001) River flow time series prediction with a range-dependent neural network. Hydrol Sci J 46:729–745
33. Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts, Melbourne
34. Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592
35. Jha R, Smakhtin V (2008) A review of methods of hydrological estimation at ungauged sites in India, vol 130. IWMI, Colombo
36. 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
37. 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
38. Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12:532–539
39. Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140
40. 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
41. Koutsoyiannis D, Langousis A (2011) Precipitation, Treatise on Water Science, edited by P. Wilderer and S. Uhlenbrook, 2, 27–78. Academic Press, Oxford
42. 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
43. Laaha G, Blöschl G (2005) Low flow estimates from short stream flow records—a comparison of methods. J Hydrol 306:264–286
44. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436
45. 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
46. Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. In: Interspeech, p 3
47. Nayak PC, Sudheer K, Rangan D, Ramasastri K (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52–66
48. Ouyang Q, Lu W (2018) Monthly rainfall forecasting using echo state networks coupled with data preprocessing methods. Water Resour Manag 32:659–674
49. Papacharalampous G, Tyralis H, Koutsoyiannis D (2018a) One-step ahead forecasting of geophysical processes within a purely statistical framework. Geosci Lett 5:12
50. 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
51. 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
52. 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
53. Pyrce R (2004) Hydrological low flow indices and their uses Watershed Science Centre, (WSC) Report
54. 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
55. 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
56. 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
57. Sang Y-F (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15
58. 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
59. Schoups G, Van de Giesen N, Savenije H (2008) Model complexity control for hydrologic prediction. Water Resources Res 44:W00B03
60. Sivapragasam C, Liong S-Y, Pasha M (2001) Rainfall and runoff forecasting with SSA–SVM approach. J Hydroinform 3:141–152
61. Sivapragasam C, Vincent P, Vasudevan G (2007) Genetic programming model for forecast of short and noisy data. Hydrol Process 21:266–272
62. Smakhtin V (2001) Low flow hydrology: a review. J Hydrol 240:147–186
63. Srikanthan R, McMahon T (2001) Stochastic generation of annual, monthly and daily climate data: a review. Hydrol Earth Syst Sci Discuss 5:653–670
64. 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
65. Toth E, Brath A, Montanari A (2000) Comparison of short-term rainfall prediction models for real-time flood forecasting. J Hydrol 239:132–147
66. Tyralis H, Koutsoyiannis D (2014) A Bayesian statistical model for deriving the predictive distribution of hydroclimatic variables. Clim Dyn 42:2867–2883
67. Tyralis H, Papacharalampous G (2017) Variable selection in time series forecasting using random forests. Algorithms 10:114
68. Tyralis H, Papacharalampous GA (2018) Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow. Adv Geosci 45:147–153
69. 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
70. WMO (2008) Manual on low flow estimation and prediction. WMO, Geneva
71. Wunsch A, Liesch T, Broda S (2018) Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). J Hydrol 567:743–758
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. Zounemat-Kermani M, Teshnehlab M (2008) Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl Soft Comput 8:928–936

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