Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran
Abstract Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River...
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2025-02-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-025-02361-0 |
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| author | M. A. Zangeneh Asadi L. Goli Mokhtari R. Zandi M. Naemitabar |
| author_facet | M. A. Zangeneh Asadi L. Goli Mokhtari R. Zandi M. Naemitabar |
| author_sort | M. A. Zangeneh Asadi |
| collection | DOAJ |
| description | Abstract Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (R 2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting R 2 values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region. |
| format | Article |
| id | doaj-art-2ff2478158624aeb89a540d9ce1f7099 |
| institution | Kabale University |
| issn | 2190-5487 2190-5495 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj-art-2ff2478158624aeb89a540d9ce1f70992025-02-09T12:49:36ZengSpringerOpenApplied Water Science2190-54872190-54952025-02-0115312310.1007/s13201-025-02361-0Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of IranM. A. Zangeneh Asadi0L. Goli Mokhtari1R. Zandi2M. Naemitabar3Department of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari UniversityDepartment of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari UniversityFaculty of Remote Sensing and GIS, Esfahan of UniversityDepartment of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari UniversityAbstract Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (R 2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting R 2 values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region.https://doi.org/10.1007/s13201-025-02361-0Suspended sediment loadMachine learningKal-e Shur Sabzevar basinNash–sutcliffePartial least squares (PLS) |
| spellingShingle | M. A. Zangeneh Asadi L. Goli Mokhtari R. Zandi M. Naemitabar Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran Applied Water Science Suspended sediment load Machine learning Kal-e Shur Sabzevar basin Nash–sutcliffe Partial least squares (PLS) |
| title | Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran |
| title_full | Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran |
| title_fullStr | Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran |
| title_full_unstemmed | Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran |
| title_short | Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran |
| title_sort | modeling evaluation and forecasting of suspended sediment load in kal e shur river sabzevar basin in northeast of iran |
| topic | Suspended sediment load Machine learning Kal-e Shur Sabzevar basin Nash–sutcliffe Partial least squares (PLS) |
| url | https://doi.org/10.1007/s13201-025-02361-0 |
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