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Machine learning prediction of sediment yield index

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Abstract

Sediment output affects soil health maintenance, reservoir sustainability, environmental contamination, and natural resource preservation. Three different algorithms, the artificial neural networks-radial basis function (ANN-RBF), Artificial Neural Networks-Multilayer Perceptron, M5P tree, were used for this purpose in the Narmada river watersheds, India. For this purpose, fifteen different scenarios are considered as inputs to the models. For selecting the best-fit model, the performance of selected models was evaluated using performance criteria such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results indicated that the ANN-RBF models outperformed the other models in terms of accuracy, with RMSE, MAE and R2 of 26.72, 19.84 and 0.98, respectively. The current study’s findings support the applicability of the proposed methodology for modeling the sediment yield index and encourage the use of these methods in alternative hydrological modeling.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/209/44.

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Authors and Affiliations

  1. WRAM Research Lab Pvt. Ltd., Nagpur, Maharashtra, 440027, India

    Sarita Gajbhiye Meshram

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

    Mohd Abul Hasan

  3. Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Atefeh Nouraki, Mohammad Alavi & Mohammad Albaji

  4. Department of Post Graduate Studies and Research in Mathematics, Jaywanti Haksar Government Post Graduate College, Chhindwara University, Betul, Madhya Pradesh, India

    Chandrashekhar Meshram

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  1. Sarita Gajbhiye Meshram
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Meshram, S.G., Hasan, M.A., Nouraki, A. et al. Machine learning prediction of sediment yield index. Soft Comput 27, 16111–16124 (2023). https://doi.org/10.1007/s00500-023-07985-5

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