River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network

  • Sultan Aljahdali
  • Alaa Sheta
  • Hamza TurabiehEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.


Forecasting Long short-term memory River Flow 


  1. 1.
    Sheta, A.F., El-Sherif, M.S.: Optimal prediction of the Nile river flow using neural networks. In: Proceedings of International Joint Conference on Neural Networks IJCNN 1999 (Cat. No.99CH36339), vol. 5, pp. 3438–3441, July 1999Google Scholar
  2. 2.
    Chen, C.-W., Oguchi, T., Hayakawa, Y.S., Saito, H., Chen, H., Lin, G.-W., Wei, L.-W., Chao, Y.-C.: Sediment yield during typhoon events in relation to landslides, rainfall, and catchment areas in Taiwan. Geomorphology 303, 540–548 (2018). Scholar
  3. 3.
    Baareh, A.K., Sheta, A.F., Khnaifes, K.A.: Forecasting river flow in the USA: a comparison between auto-regression and neural network non-parametric models. J. Comput. Sci. 2(10), 775–780 (2006)CrossRefGoogle Scholar
  4. 4.
    Sharafati, A., Zahabiyoun, B.: Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arab. J. Sci. Eng. 39(10), 6835–6849 (2014). Scholar
  5. 5.
    Roy, P., Choudhury, P., Saharia, M.: Dynamic ANN modeling for flood forecasting in a river network, vol. 1298 (2010)Google Scholar
  6. 6.
    Dawson, C.W., Wilby, R.: An artificial neural network approach to rainfall-runoff modelling. Hydrol. Sci. J. 43(1), 47–66 (1998). Scholar
  7. 7.
    Kerh, T., Lee, C.: Neural networks forecasting of flood discharge at an unmeasured station using river upstream information. Adv. Eng. Softw. 37(8), 533–543 (2006). Scholar
  8. 8.
    Can, B., Tosunoğlu, F., Kahya, E.: Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of çoruh basin. Turkey. Water Environ. J. 26(4), 567–576 (2012). Scholar
  9. 9.
    Koza, J., Koza, J., Rice, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. A Bradford Book, Bradford (1992). Scholar
  10. 10.
    Babovic, V., Keijzer, M.: Rainfall runoff modelling based on genetic programming. Hydrol. Res. 33(5), 331–346 (2002). Scholar
  11. 11.
    Babovic, V., Abbott, M.B.: The evolution of equations from hydraulic data part i: theory. J. Hydraul. Res. 35(3), 397–410 (1997). Scholar
  12. 12.
    Nourani, V., Komasi, M., Alami, M.T.: Hybrid wavelet–genetic programming approach to optimize ann modeling of rainfall–runoff process. J. Hydrol. Eng. 17(6), 724–741 (2012)CrossRefGoogle Scholar
  13. 13.
    Ghorbani, M.A., Khatibi, R., Aytek, A., Makarynskyy, O., Shiri, J.: Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput. Geosci. 36(5), 620–627 (2010). Scholar
  14. 14.
    Sugeno, M.: An introductory survey of fuzzy control. Inf. Sci. 36(1), 59–83 (1985). Scholar
  15. 15.
    Corani, G., Guariso, G.: Coupling fuzzy modeling and neural networks for river flood prediction. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(3), 382–390 (2005)CrossRefGoogle Scholar
  16. 16.
    Al-Zu’i, Y., Sheta, A., Al-Zu’i, J.: Nile river flow forecasting based Takagi-Sugeno fuzzy model. J. Appl. Sci. 10, 284–290 (2010)Google Scholar
  17. 17.
    Wu, F., Shi, Q., Hasan, S.S., Shi, C., Gibson, J.: Urbanization and Industrial Transformation for Improved Water Management, pp. 61–89. Springer, Singapore (2019). Scholar
  18. 18.
    Le, X.-H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 849–862 (2019)CrossRefGoogle Scholar
  19. 19.
    Zhang, X.Y., Yin, F., Zhang, Y.M., Liu, C.L., Bengio, Y.: Drawing and recognizing chinese characters with recurrent neural network. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2017) Google Scholar
  20. 20.
    Turabieh, H., Mafarja, M., Li, X.: Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert Syst. Appl. 122, 27–42 (2019). Scholar
  21. 21.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). Scholar
  22. 22.
    Majhi, B., Naidu, D., Mishra, A.P., Satapathy, S.C.: Improved prediction of daily pan evaporation using deep-LSTM model. Neural Comput. Appl. (2019).
  23. 23.
    Asadi-Aghbolaghi, M., Clapés, A., Bellantonio, M., Escalante, H.J., Ponce-López, V., Baró, X., Guyon, I., Kasaei, S., Escalera, S.: Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey, pp. 539–578. Springer International Publishing, Cham (2017). Scholar
  24. 24.
    Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H.: Enhancing and combining sequential and tree LSTM for natural language inference. CoRR abs/1609.06038 (2016).
  25. 25.
    Akandeh, A., Salem, M.: Simplified long short-term memory recurrent neural networks: part II. CoRR, abs/1707.04623 (2017).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceTaif UniversityTaifSaudi Arabia
  2. 2.Computer Science DepartmentSouthern Connecticut State UniversityNew HavenUSA
  3. 3.Department of Information TechnologyTaif UniversityTaifSaudi Arabia

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