Mir Jafar Sadegh SafariSarita Gajbhiye MeshramKhabat KhosraviAdel MoatamedSafari, Mir Jafar SadeghMeshram, Sarita GajbhiyeKhosravi, KhabatMoatamed, Adel2025-10-06202200334553, 142091360033-45531420-913610.1007/s00024-022-03131-82-s2.0-85139123204https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139123204&doi=10.1007%2Fs00024-022-03131-8&partnerID=40&md5=4a833096f6a6a45006c09a4d075b46edhttps://gcris.yasar.edu.tr/handle/123456789/8656https://doi.org/10.1007/s00024-022-03131-8Suspended 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. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessIsotonic Regression, Iterative Classifier Optimizer, Mahanadi River, Sequential Minimal Optimization Regression, Suspended Sediment, Iterative Methods, Mean Square Error, Optimization, Regression Analysis, Isotonic Regression, Iterative Classifier Optimizer, Mahanadi, Mahanadi River, Optimizers, Regression Modelling, Root Mean Square Errors, Sequential Minimal Optimization, Sequential Minimal Optimization Regression, Suspended Sediment Loads, Suspended Sediments, Accuracy Assessment, Algorithm, Classification, Efficiency Measurement, Fluvial Deposit, Hydrological Modeling, Optimization, Regression Analysis, River Discharge, Sediment Transport, Suspended Sediment, Chhattisgarh, India, Mahanadi RiverIterative methods, Mean square error, Optimization, Regression analysis, Isotonic regression, Iterative classifier optimizer, Mahanadi, Mahanadi river, Optimizers, Regression modelling, Root mean square errors, Sequential minimal optimization, Sequential minimal optimization regression, Suspended sediment loads, Suspended sediments, accuracy assessment, algorithm, classification, efficiency measurement, fluvial deposit, hydrological modeling, optimization, regression analysis, river discharge, sediment transport, suspended sediment, Chhattisgarh, India, Mahanadi RiverIsotonic RegressionMahanadi RiverSuspended SedimentSequential Minimal Optimization RegressionIterative Classifier OptimizerArticle