Monthly runoff forecasting based on LSTM–ALO model

  • Xiaohui YuanEmail author
  • Chen Chen
  • Xiaohui LeiEmail author
  • Yanbin Yuan
  • Rana Muhammad Adnan
Original paper


Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.


Monthly runoff forecasting Long short-term memory neural network Ant lion optimizer Errors decomposition 



This work was supported by National Natural Science Foundation of China (Nos. U1765201, 41571514, 51379080), Hubei Provincial Collaborative Innovation Center for New Energy Microgrid in China Three Gorges University, and the Fundamental Research Funds for the Central Universities (No. 2017KFYXJJ204).


  1. Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390(1):85–91CrossRefGoogle Scholar
  2. Anderson PL, Meerschaert MM, Zhang K (2013) Forecasting with prediction intervals for periodic autoregressive moving average models. J Time Ser Anal 34(2):187–193CrossRefGoogle Scholar
  3. Bukhari D, Wang Y, Wang H (2017) Multilingual convolutional long short-term memory, deep neural networks for low resource speech recognition. Proc Comput Sci 107:842–847CrossRefGoogle Scholar
  4. Chen ZH, Yuan XH, Ji B, Wang PT, Tian H (2014) Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II. Energy Convers Manag 84:390–404CrossRefGoogle Scholar
  5. De Giorgi MG, Campilongo S, Ficarella A (2014) Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7(8):5251–5272CrossRefGoogle Scholar
  6. El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resour Manag 21(3):533–556CrossRefGoogle Scholar
  7. Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471CrossRefGoogle Scholar
  8. Greff K, Srivastava RK, Koutník J (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 99:1–11Google Scholar
  9. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefGoogle Scholar
  10. Ji B, Yuan XH, Li XS, Huang YH, Li WW (2014) Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration. Energy Convers Manag 87:589–598CrossRefGoogle Scholar
  11. Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8CrossRefGoogle Scholar
  12. Kalteh AM, Chen G (2014) Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting. J Hydrol 519:2822–2831CrossRefGoogle Scholar
  13. Kisi O (2015) Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water Resour Manag 29(14):5109–5127CrossRefGoogle Scholar
  14. Kong XM, Huang GH, Fan YR (2015) Maximum entropy-gumbel-hougaard copula method for simulation of monthly streamflow in Xiangxi river, China. Stoch Environ Res Risk Assess 29(3):833–846CrossRefGoogle Scholar
  15. Liang J, Yuan XH, Yuan YB, Chen ZH, Li YZ (2017) Nonlinear dynamic analysis and robust controller design for Francis hydraulic turbine regulating system with a straight-tube surge tank. Mech Syst Signal Process 85:927–946CrossRefGoogle Scholar
  16. Mccuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe efficiency index. J Hydrol Eng 11(6):597–602CrossRefGoogle Scholar
  17. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98CrossRefGoogle Scholar
  18. Okkan U, Serbes ZA (2012) Rainfall–runoff modeling using least squares support vector machines. Environmetrics 23(6):549–564CrossRefGoogle Scholar
  19. Seidou O, Ouarda TBMJ (2007) Recursion-based multiple change point detection in multiple linear regression and application to river streamflows. Water Resour Res 43(7):W07404. CrossRefGoogle Scholar
  20. Sharma S, Srivastava P, Fang X (2015) Performance comparison of adoptive neuro fuzzy inference system (ANFIS) with loading simulation program C++ (LSPC) model for streamflow simulation in El Nino southern oscillation (ENSO)-affected watershed. Expert Syst Appl 42(4):2213–2223CrossRefGoogle Scholar
  21. Sudheer CH, Anand N, Panigrahi BK (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23CrossRefGoogle Scholar
  22. Tahir AA, Chevallier P, Arnaud Y, Ashraf M, Bhatti MT (2015) Snow cover trend and hydrological characteristics of the Astore River basin (Western Himalayas) and its comparison to the Hunza basin (Karakoram region). Sci Total Environ 505:748–761CrossRefGoogle Scholar
  23. Wang W, Van Gelder PH, Vrijling JK (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324(1):383–399CrossRefGoogle Scholar
  24. Yaseen ZM, El-Shafie A, Jaafar O (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844CrossRefGoogle Scholar
  25. Yuan XH, Ji B, Zhang SQ, Tian H, Chen ZH (2014) An improved artificial physical optimization algorithm for dynamic dispatch of generators with valve-point effects and wind power. Energy Convers Manag 82:92–105CrossRefGoogle Scholar
  26. Yuan XH, Tian H, Yuan YB, Huang YH, Ikram RM (2015a) An extended NSGA-III for solution multi-objective hydro-thermal-wind scheduling considering wind power cost. Energy Convers Manag 96:568–578CrossRefGoogle Scholar
  27. Yuan XH, Ji B, Yuan YB, Ikram RM, Zhang XP, Huang YH (2015b) An efficient chaos embedded hybrid approach for hydro-thermal unit commitment problem. Energy Convers Manag 91:225–237CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.College of Electrical Engineering and New EnergyChina Three Gorges UniversityYichangChina
  3. 3.State Key Laboratory of Simulation and Regulation of Water Cycle in River BasinChina Institute of Water Resources and Hydropower ResearchBeijingChina
  4. 4.School of Resource and Environmental EngineeringWuhan University of TechnologyWuhanChina

Personalised recommendations