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A comparative study between dynamic and soft computing models for sediment forecasting

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Abstract

Runoff–sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall–runoff–sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily–sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge–sediment data for 25 years were obtained from Shakkar Watershed; Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash–Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure.

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Acknowledgements

The Authors extend their thanks to the Deanship of Scientific Research at King Khalid University for funding this work through the small research groups under grant number RGP. 1/372/42.

Funding

This research work was supported by the Deanship of Scientific Research at King Khalid University under Grant number RGP. 1/372/42.

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Authors and Affiliations

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

    Sarita Gajbhiye Meshram

  2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  3. Faculty of Engineering Department of Civil Engineering, Baze University, Abuja, Nigeria

    S. I. Abba

  4. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

    Ehsan Alvandi

  5. Department of Post Graduate Studies and Research in Mathematics, Jayawanti Haksar Government Post Graduation College of Chhindwara University, Betul, India

    Chandrashekhar Meshram

  6. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia

    Khaled Mohamed Khedher

  7. Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, 8000, Nabeul, Tunisia

    Khaled Mohamed Khedher

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  1. Sarita Gajbhiye Meshram
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  2. Hamid Reza Pourghasemi
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  3. S. I. Abba
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  5. Chandrashekhar Meshram
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  6. Khaled Mohamed Khedher
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Correspondence to Sarita Gajbhiye Meshram.

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Meshram, S.G., Pourghasemi, H.R., Abba, S.I. et al. A comparative study between dynamic and soft computing models for sediment forecasting. Soft Comput 25, 11005–11017 (2021). https://doi.org/10.1007/s00500-021-05834-x

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