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Relevance vector machines approach for long-term flow prediction

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Abstract

Over the past years, some artificial intelligence techniques like artificial neural networks have been widely used in the hydrological modeling studies. In spite of their some advantages, these techniques have some drawbacks including possibility of getting trapped in local minima, overtraining and subjectivity in the determining of model parameters. In the last few years, a new alternative kernel-based technique called a support vector machines (SVM) has been found to be popular in modeling studies due to its advantages over popular artificial intelligence techniques. In addition, the relevance vector machines (RVM) approach has been proposed to recast the main ideas behind SVM in a Bayesian context. The main purpose of this study is to examine the applicability and capability of the RVM on long-term flow prediction and to compare its performance with feed forward neural networks, SVM, and multiple linear regression models. Meteorological data (rainfall and temperature) and lagged data of rainfall were used in modeling application. Some mostly used statistical performance evaluation measures were considered to evaluate models. According to evaluations, RVM method provided an improvement in model performance as compared to other employed methods. In addition, it is an alternative way to popular soft computing methods for long-term flow prediction providing at least comparable efficiency.

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Acknowledgments

The authors feel responsible to thank the reviewers of Neural Computing & Applications and Gul Inan (from Middle East Technical University) for their valuable comments and contributions to the revision of this study.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering-Architecture, Balikesir University, Balıkesir, Turkey

    Umut Okkan

  2. Farm Structures and Irrigation Department, Faculty of Agriculture, Ege University, Izmir, Turkey

    Zafer Ali Serbes

  3. Centre for Disaster Mitigation and Management, VIT University, Vellore, India

    Pijush Samui

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  1. Umut Okkan
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  2. Zafer Ali Serbes
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  3. Pijush Samui
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Correspondence to Umut Okkan.

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Okkan, U., Serbes, Z.A. & Samui, P. Relevance vector machines approach for long-term flow prediction. Neural Comput & Applic 25, 1393–1405 (2014). https://doi.org/10.1007/s00521-014-1626-9

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  • DOI: https://doi.org/10.1007/s00521-014-1626-9

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