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A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction

Author

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  • Ahmad Khazaee Poul

    (Shahid Beheshti University)

  • Mojtaba Shourian

    (Shahid Beheshti University)

  • Hadi Ebrahimi

    (University of Qom)

Abstract

Reliable and precise prediction of the rivers flow is a major concern in hydrologic and water resources analysis. In this study, multi-linear regression (MLR) as a statistical method, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) as non-linear ones and K-nearest neighbors (KNN) as a non-parametric regression method are applied to predict the monthly flow in the St. Clair River between the US and Canada. In the developed methods, six scenarios for input combinations are defined in order to study the effect of different input data on the outcomes. Performances of the models are evaluated using statistical indices as the performance criteria. Results obtained show that adding lag times of flow, temperature and precipitation to the inputs improve the accuracy of the predictions significantly. For a further investigation, the aforementioned models are coupled with wavelet transform. Using the wavelet transform improves the values of Nash-Sutcliff coefficient to 0.907, 0.930, 0.923, and 0.847 from 0.340, 0.404, 0.376 and 0.419 respectively, by coupling it with MLR, ANN, ANFIS, and KNN models.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:8:d:10.1007_s11269-019-02273-0
    DOI: 10.1007/s11269-019-02273-0
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. Rajeev Sahay & Ayush Srivastava, 2014. "Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 301-317, January.
    3. Jaydip Makwana & Mukesh Tiwari, 2014. "Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4857-4873, October.
    4. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    5. Zaw Latt & Hartmut Wittenberg, 2014. "Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2109-2128, June.
    6. 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.
    7. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
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    6. Subbarayan Saravanan & Nagireddy Masthan Reddy & Quoc Bao Pham & Abdullah Alodah & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi, 2023. "Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset," Sustainability, MDPI, vol. 15(16), pages 1-26, August.
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    10. Wenxin Xu & Jie Chen & Xunchang J. Zhang, 2022. "Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3609-3625, August.
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