Abstract
Streamflow is one of the complex nonlinear systems in hydrological science, and modeling of such systems provides significant information for the water resource management, optimization, and agriculture process. This study utilized some efficient machine learning models such as extreme learning machine (ELM) and artificial neural network (ANN) with or without wavelet used for denoising (ELMwl) and (ANNwl). To develop various dynamic predictive models which are based on the historical configurations of nonlinear autoregressive (NAR) structure, lag functional models are employed for the mean monthly upstream flow observations of the Tarbela Dam on the Indus River basin. The comparative study shows the predicting performance of the applied functional models based on error analysis such as root-mean-square error, mean-absolute error, and coefficient of determination. The analysis revealed that the integrated models ELMwl and ANNwl represented a significant accuracy as compared to other intelligent models which show that wavelet transformation develops the forecasting adequacy so that it may be useful for water resource management or operators.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14(11–12):2157–2172
Adamowski J (2007) Development of a short-term river flood forecasting method based on wavelet analysis. Warsaw, Poland. Polish Academy of Sciences. https://doi.org/10.1016/j.jhydrol.2008.02.013
Anctil F, Tape D (2004) An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J Environ Eng Sci 3:121–128
Aslan MF, Sabancı K, Durdu A (2017) Different wheat species classifier application of ANN and ELM. J Multidiscip Eng Sci Technol (JMEST) 4(9)
Cannas B, Fanni A, Sias G, Tronei S, Zedda MK (2006) River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European Geosciences Union
DanandehMehr A, Kahya E, Şahin A, Nazemosadat MJ (2014) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-014-0613-0
Dawson CW, Abrahart RJ, See LM (2007) Hydro Test: a web-based toolbox of evaluation metrics for the standardized assessment of hydrological forecasts. Environ Model Softw 22(7):1034–1052
Deo RC, Şahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188(2):1–24. https://doi.org/10.1007/s10661-016-5094-9
Gallant SI (1993) Neural network learning and expert systems. MIT Press
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Kim TW, Valdes JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng 8(6):319–328. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(319)
Kişi Ö (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60–63
Kişi Ö (2008) Streamflow forecasting using the neuro-wavelet technique. Hydrol Process 22(20):4142–4152
Kişi Ö (2009) Neural networks and wavelet conjunction model for intermittent streamflow forecasting. J Hydrol Eng 14(8):773–782
Li B, Cheng C (2014) Monthly discharge forecasting using wavelet neural networks with extreme learning machines. Sci China Technol Sci 57(12):2441–2452
Luger GF (2005) Artificial intelligence: structures and strategies for complex problem solving, Fifth edn. Addison-Wesley. https://doi.org/10.1017/s0269888900006858
Partal T (2009) River flow forecasting using different artificial neural network algorithms and wavelet transform. Can J Civ Eng 36:26–38. https://doi.org/10.1139/l08-090
Siddiqi TA, Inayatullah S, Hassan SA, Naz S, Iqbal ST, Ahsanuddin M (2019) Nonlinear structure-based artificial neural computing for upstream flow functional models. Eur Sci J 15(15): 1-11
Tayyab M, Zhou J, Zeng X, Adnan R (2016) Discharge forecasting by applying artificial neural networks at the Jinsha river basin, China. Eur Sci J 12(9):108–127
Tiwari M, Chatterjee C (2011) A new wavelet-bootstrap- ANN hybrid model for daily discharge forecasting. J Hydroinf 13(3):500–519
Tiwari MK, Song KY, Chatterjee C, Gupta MM (2013) Improving the reliability of river flow forecasting using neural networks, wavelets, and self-organizing maps. J Hydroinf 15(2):486–502
Wu C, Chau K, Li Y (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372:80–93. https://doi.org/10.1016/j.jhydrol.2009.03.038
Yaseen ZM, El-Shafie A, Jaafar O, Afan HA, Sayl KN (2015) Artificial intelligence-based models for streamflow forecasting: 2000–2015. J Hydrol 530:829–844
Acknowledgements
We are thankful to the Irrigation Department, Sindh, Pakistan for providing the historical time series of gauge observations of upstream flow at Tarbela Dam on the Indus River basin.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authros have no relevant financial interest or support to disclose.
Conflict of interest
The author(s) declare that they have no competing interests.
Additional information
Responsible Editor: Zhihua Zhang
Rights and permissions
About this article
Cite this article
Siddiqi, T.A., Ashraf, S., Khan, S.A. et al. Estimation of data-driven streamflow predicting models using machine learning methods. Arab J Geosci 14, 1058 (2021). https://doi.org/10.1007/s12517-021-07446-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07446-z