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Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms

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Abstract

Support Vector Regression (SVR) combined with Invasive Weeds Optimization (IWO), standalone SVR, and Radial Basis Function Neural Networks are applied to estimate channel sinuosity in perennial rivers. With this aim, a dataset with 132 sinuosity data and related geomorphologic data, corresponding to 119 perennial streams, is considered. Bayesian Mutual Information theory is used to determine the parameters affecting channel sinuosity to reveal that bankfull depth affects sinuosity the most. Seven input parameter combinations for sinuosity prediction are considered, and in both training and testing stages, the SVR-IWO model \(\left( {R_{Train} = 0.959,RMSE_{Train} = 0.072, MAE_{Train} = 0.037, R_{test} = 0.892, RMSE_{Test} = 0.103, MAE_{Test} = 0.065} \right)\) shows the best prediction performance while the standalone SVR model generated the results with performances of \(\left( {R_{Train} = 0.792,RMSE_{Train} = 0.158, MAE_{Train} = 0.141, R_{test} = 0.704, RMSE_{Test} = 0.163, MAE_{Test} = 0.151} \right)\). Model prediction uncertainty is quantified in terms of entropy for the three models considered, further confirming that the sinuosity set predicted by the SVR-IWO model is the closest to the observed set.

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Authors and Affiliations

  1. Department of Structural Mechanics & Hydraulics Engineering, University of Granada, 18001, Granada, Spain

    Masoud Haghbin

  2. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ahmad Sharafati

  3. Department of Mechanical and Construction Engineering, Northumbria University, Wynne Jones Building, Newcastle upon Tyne, NE1 8ST, UK

    Davide Motta

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  1. Masoud Haghbin
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  2. Ahmad Sharafati
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Contributions

MH proposed the topic, carried out the investigation, modeling and participated in drafting the manuscript. AS participated in coordination, aided in the interpretation of results, and paper editing. DM helped in data gathering, carried out the visualization and paper editing. All authors read and approved the final manuscript.

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Correspondence to Ahmad Sharafati.

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Communicated by H. Babaie.

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Haghbin, M., Sharafati, A. & Motta, D. Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms. Earth Sci Inform 14, 2279–2292 (2021). https://doi.org/10.1007/s12145-021-00682-7

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