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Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS

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Abstract

Modeling suspended sediment load is a critical element of water resources engineering. In this work, using the ANFIS method, everyday suspended sediment particles were estimated in different categories of the river in US Sediment big data, and various flow rates were utilized for testing and training. The artificial intelligent (AI) method called ANFIS is used to train actual data from the river and provide an AI model with artificial data points. This artificial data point can show the occurrence of disaster for a critical day with different flow rates. The changing parameter in the AI model enables us to make a correct decision about critical time for rivers. This study also concentrates on the sensitivity investigation of ANFIS setting parameters on the accurateness of numerical results in order to find the best ANFIS model for rapid oscillation in the data set. The best performance of the ANFIS method is achieved with the trimf membership function, the number of input membership function = 16, and the number of iteration = 1000. The results also showed that the ANFIS model can provide fast computational calculation, and adding more nodes for the prediction cannot change the overall time of calculation due to the meshless behavior of the model. In addition to this model, we used the ant colony method for training of data set, and we found that the ANFIS method is better in learning and prediction of the dataset.

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Acknowledgements

This work was supported by the Government of the Russian Federation (Act 211, contract 02.A03.21.0011) and by the Ministry of Science and Higher Education of Russia (grant FENU-2020-0019).

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Meisam Babanezhad

  2. Faculty of Electrical and Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Meisam Babanezhad

  3. Faculty of Mechanical and Energy Engineering Department, Shahid Beheshti University, Tehran, Iran

    Iman Behroyan

  4. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Azam Marjani

  5. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Azam Marjani

  6. Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland

    Saeed Shirazian

  7. Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin Prospekt, Chelyabinsk, Russia, 454080

    Saeed Shirazian

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  1. Meisam Babanezhad
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  2. Iman Behroyan
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  3. Azam Marjani
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  4. Saeed Shirazian
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Correspondence to Azam Marjani.

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Babanezhad, M., Behroyan, I., Marjani, A. et al. Artificial intelligence simulation of suspended sediment load with different membership functions of ANFIS. Neural Comput & Applic 33, 6819–6833 (2021). https://doi.org/10.1007/s00521-020-05458-6

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  • DOI: https://doi.org/10.1007/s00521-020-05458-6

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