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Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

Author

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  • Ali Ahani

    (Shahid Beheshti University)

  • Mojtaba Shourian

    (Shahid Beheshti University)

  • Peiman Rahimi Rad

    (Shahid Beheshti University)

Abstract

In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.

Suggested Citation

  • Ali Ahani & Mojtaba Shourian & Peiman Rahimi Rad, 2018. "Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 383-399, January.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:2:d:10.1007_s11269-017-1792-5
    DOI: 10.1007/s11269-017-1792-5
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    References listed on IDEAS

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    1. Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    2. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
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    Cited by:

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    2. Lingqi Li & Kai Wu & Enhui Jiang & Huijuan Yin & Yuanjian Wang & Shimin Tian & Suzhen Dang, 2021. "Evaluating Runoff-Sediment Relationship Variations Using Generalized Additive Models That Incorporate Reservoir Indices for Check Dams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3845-3860, September.
    3. Yinghui Meng & Sultan Noman Qasem & Manouchehr Shokri & Shahab S, 2020. "Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    4. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    5. Alireza Emadi & Reza Sobhani & Hossein Ahmadi & Arezoo Boroomandnia & Sarvin Zamanzad-Ghavidel & Hazi Mohammad Azamathulla, 2022. "Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(4), pages 4845-4871, April.
    6. MacMillan, Andrew & Schell, Kristen R. & Roughley, Colter, 2023. "A predictive model of velocity for local hydrokinetic power assessment based on remote sensing data," Renewable Energy, Elsevier, vol. 211(C), pages 285-295.
    7. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    8. Saeid Mehdizadeh & Ali Kozekalani Sales, 2018. "A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3001-3022, July.
    9. Ran-Ran He & Yuanfang Chen & Qin Huang & Zheng-Wei Pan & Yong Liu, 2020. "Predictability of Monthly Streamflow Time Series and its Relationship with Basin Characteristics: an Empirical Study Based on the MOPEX Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4991-5007, December.

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