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Suspended Sediment Modeling Using Sequential Minimal Optimization Regression and Isotonic Regression Algorithms Integrated with an Iterative Classifier Optimizer

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Abstract

Suspended sediment load modeling through advanced computational algorithms is of major importance and a challenging topic for developing highly accurate hydrological models. To model the suspended sediment load in the Rampur watershed station in the Mahanadi River Basin, Chhattisgarh State, India, unique integrated computational intelligence regression models with an optimizer are proposed in this study. For the first time in the literature, the isotonic regression (ISO) and sequential minimal optimization regression (SMOR) models and their hybrid versions with an iterative classifier optimizer (ICO) are applied for suspended sediment load modeling. The research is based on daily discharge and suspended sediment data collected over a 38-year period (1976–2014). Root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) were employed to evaluate the performance of the standalone ISO and SMOR, as well as the proposed ICO–ISO and ICO–SMOR hybrid models. Ten different scenarios were considered for modeling to investigate the performance of the models using different input combinations. The proposed new models were found to be more reliable than standalone ISO and SMOR models. Results revealed that the performance of the hybrid model was mostly attributable to the basic algorithm for the model development, where both SMOR and ICO–SMOR models were superior to their ISO and ICO–ISO counterparts in terms of accurate computation. Overall, the ICO–SMOR models outperformed the other models in terms of accuracy, with RMSE, RRMSE, R2, and NSE of 5495.1 tons/day, 2.77, 0.90, and 0.86, respectively. The current study's findings support the applicability of the proposed methodology for modeling of suspended sediment load 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 small research groups under grant number RGP. 1/113/43. Special thanks to Mr. Behzad Shakouri from Urmia University for his help during the revision of the manuscript.

Funding

This research work was supported by the Deanship of Scientific Research at King Khalid University under Grant number RGP. 1/113/43.

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Authors and Affiliations

  1. Department of Civil Engineering, Yaşar University, Izmir, Turkey

    Mir Jafar Sadegh Safari

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

    Sarita Gajbhiye Meshram

  3. Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Khabat Khosravi

  4. Department of Earth and Environment, Florida International University, Miami, USA

    Khabat Khosravi

  5. Department of Geography, College of Humanities, King Khalid University, Abha, Saudi Arabia

    Adel Moatamed

  6. Department of Geography, Faculty of Arts, Assiut University, Assiut, Egypt

    Adel Moatamed

  7. Prince Sultan Bin Abdul-Aziz Center for Environment and Tourism Research and Studies, King Khalid University, Abha, Saudi Arabia

    Adel Moatamed

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  1. Mir Jafar Sadegh Safari
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Safari, M.J.S., Meshram, S.G., Khosravi, K. et al. Suspended Sediment Modeling Using Sequential Minimal Optimization Regression and Isotonic Regression Algorithms Integrated with an Iterative Classifier Optimizer. Pure Appl. Geophys. 179, 3751–3765 (2022). https://doi.org/10.1007/s00024-022-03131-8

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