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A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

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  • Shivshanker Patel
  • Parthasarathy Ramachandran

Abstract

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive–loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE). Copyright Springer Science+Business Media Dordrecht 2015

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  • Shivshanker Patel & Parthasarathy Ramachandran, 2015. "A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 589-602, January.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:2:p:589-602
    DOI: 10.1007/s11269-014-0705-0
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    1. G. E. P. Box & G. M. Jenkins & J. F. MacGregor, 1974. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 158-179, June.
    2. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
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    Cited by:

    1. José-Luis Molina & Santiago Zazo, 2017. "Causal Reasoning for the Analysis of Rivers Runoff Temporal Behavior," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4669-4681, November.
    2. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    3. Robyn Horan & Pawan S. Wable & Veena Srinivasan & Helen E. Baron & Virginie J. D. Keller & Kaushal K. Garg & Nathan Rickards & Mike Simpson & Helen A. Houghton-Carr & H. Gwyn Rees, 2021. "Modelling Small-Scale Storage Interventions in Semi-Arid India at the Basin Scale," Sustainability, MDPI, vol. 13(11), pages 1-28, May.
    4. Hakan Tongal & Martijn J. Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
    5. Hakan Tongal & Martijn Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
    6. 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.
    7. Yajie Wu & Yuan Chen & Yong Tian, 2022. "Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    8. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
    9. Zhangjun Liu & Shenglian Guo & Honggang Zhang & Dedi Liu & Guang Yang, 2016. "Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2111-2126, May.
    10. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.

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