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A stream prediction model based on attention-LSTM

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Abstract

The small- and medium-sized watersheds have complex and varied hydrogeological features, boundary conditions, and human activities. There are nonlinear interactions between these factors, which leads to great challenges in predicting the stream of the river. Since not all factors are positively correlated with flood forecasting, and irrelevant factors tend to bring a lot of noise, it is necessary to give more attention to the absolute action factors. In this paper, we forecast the flow values over the next 12 hours, using an Attention-LSTM prediction model with an attention mechanism based on long-term and short-term memory networks that consider past stream data, past weather data, and weather forecasts data. We use data from Tunxi watershed, China, and evaluate the model with root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2). The forecast results of the Attention-LSTM model are compared with the prediction results of two traditional machine learning models and an LSTM model. The experimental results show that the Attention-LSTM model has a higher score, and provided a new method for flood forecasting.

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References

  • Abrahart RJ, See LM (2007) Neural network modelling of non-linear hydrological relationships

  • Adikari Y, Yoshitani J (2009) Global trends in water-related disasters: an insight for policymakers. World Water Assessment Programme Side Publication Series, Insights. The United Nations, UNESCO. International Centre for Water Hazard and Risk Management (ICHARM)

  • Asadieh B, Krakauer NY (2015) Global trends in extreme precipitation: climate models versus observations

  • Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473

  • Chang F-J, Chen Y-C (2001) A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of hydrology 245(1-4):153–164

    Article  Google Scholar 

  • Cheng J, Dong L, Lapata M (2016) Long short-term memory-networks for machine reading. arXiv:1601.06733

  • Chorowski JK, Bahdanau D, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. In: Advances in neural information processing systems, pp 577–585

  • Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216

    Article  Google Scholar 

  • Diop L, Bodian A, Djaman K, Yaseen ZM, Deo RC, El-Shafie A, Brown LC (2018) The influence of climatic inputs on stream-flow pattern forecasting: case study of upper senegal river. Environmental earth sciences 77(5):182

    Article  Google Scholar 

  • Ghorbani MA, Khatibi R, Karimi V, Yaseen ZM, Zounemat-Kermani M (2018) Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows. Water resources management 32(13):4201–4215

    Article  Google Scholar 

  • Ghose D, Das U, Roy P (2018) Modeling response of runoff and evapotranspiration for predicting water table depth in arid region using dynamic recurrent neural network. Groundwater for Sustainable Development 6:263–269

    Article  Google Scholar 

  • Graves A, Schmidhuber J (2009) Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in neural information processing systems, pp 545–552

  • Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J, et al. (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. A field guide to dynamical recurrent neural networks. IEEE Press

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation 9 (8):1735–1780

    Article  Google Scholar 

  • Hsu K-l, Gupta HV, Gao X, Sorooshian S, Imam B (2002) Self-organizing linear output map (solo): An artificial neural network suitable for hydrologic modeling and analysis. Water Resour Res 38(12):38–1

    Article  Google Scholar 

  • Jothiprakash V, Magar RB (2012) Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. Journal of hydrology 450:293–307

    Article  Google Scholar 

  • Kentel E (2009) Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates. Journal of hydrology 375(3-4):481–488

    Article  Google Scholar 

  • Kisi O, Choubin B, Deo RC, Yaseen ZM (2019) Incorporating synoptic-scale climate signals for streamflow modelling over the mediterranean region using machine learning models. Hydrol Sci J 64(10):1240–1252

    Article  Google Scholar 

  • Liu F, Xu F, Yang S (2017) A flood forecasting model based on deep learning algorithm via integrating stacked autoencoders with bp neural network. In: 2017 IEEE third International conference on multimedia big data (BigMM), pp 58–61. IEEE

  • Luong M-T, Le QV, Sutskever I, Vinyals O, Kaiser L (2015) Multi-task sequence to sequence learning. arXiv:1511.06114

  • Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Yaseen ZM (2019) Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J Hydrol 575:544–556

    Article  Google Scholar 

  • Paniconi C, Putti M (2015) Physically based modeling in catchment hydrology at 50: Survey and outlook. Water Resour Res 51(9):7090–7129

    Article  Google Scholar 

  • Salih SQ, Allawi MF, Yousif AA, Armanuos AM, Saggi MK, Ali M, Shahid S, Al-Ansari N, Yaseen ZM, Chau K-W (2019) Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of nasser lake in Egypt. Engineering Applications of Computational Fluid Mechanics 13(1):878–891

    Article  Google Scholar 

  • Salih SQ, Sharafati A, Khosravi K, Faris H, Kisi O, Tao H, Ali M, Yaseen ZM (2020) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrol Sci J 65(4):624–637

    Article  Google Scholar 

  • Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13(3):219–231

    Article  Google Scholar 

  • Shoaib M, Shamseldin AY, Melville BW, Khan MM (2016) A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. J Hydrol 535:211–225

    Article  Google Scholar 

  • Terzi O, Ergin G (2014) Forecasting of monthly river flow with autoregressive modeling and data-driven techniques. Neural Comput & Applic 25(1):179–188

    Article  Google Scholar 

  • Todini E (2007) Hydrological catchment modelling: past, present and future. Hydrol Earth Syst Sci 11(1):468–482

    Article  Google Scholar 

  • Tung TM, Yaseen ZM, et al. (2020) A survey on river water quality modelling using artificial intelligence models: 2000–2020. J Hydrol 585:124670

    Article  Google Scholar 

  • Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the arma, arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of dez dam reservoir. Journal of hydrology 476:433–441

    Article  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  • Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057

  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480–1489

  • 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, Mohtar WHMW, Ameen AMS, Ebtehaj I, Razali SFM, Bonakdari H, Salih SQ, Al-Ansari N, Shahid S (2019) Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: Case study in tropical region. IEEE Access 7:74471–74481

    Article  Google Scholar 

  • Yaseen ZM, Naganna SR, Sa’adi Z, Samui P, Ghorbani MA, Salih SQ, Shahid S (2020) Hourly river flow forecasting: Application of emotional neural network versus multiple machine learning paradigms. Water Resour Manag 34(3):1075–1091

    Article  Google Scholar 

  • Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (lstm) based model for predicting water table depth in agricultural areas. Journal of hydrology 561:918–929

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Le Yan & Changwei Chen

  2. Computer and Information College, Hohai University, Nanjing, 211100, China

    Le Yan & Tingting Hang

  3. Key Laboratory of Unmanned Aerial Vehicle Development, Data Application of Anhui Higher Education Institutes, Wanjiang University of Technology, Maanshan, 243031, China

    Tingting Hang

  4. College of Letters and Science, University of California, Los Angeles, United States

    Youchuan Hu

Authors
  1. Le Yan
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  2. Changwei Chen
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  3. Tingting Hang
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  4. Youchuan Hu
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Corresponding author

Correspondence to Changwei Chen.

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Communicated by: H. Babaie

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Yan, L., Chen, C., Hang, T. et al. A stream prediction model based on attention-LSTM. Earth Sci Inform 14, 723–733 (2021). https://doi.org/10.1007/s12145-021-00571-z

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