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Open AccessArticle

Prediction of Suspended Sediment Load Using Data-Driven Models

College of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman 761, Iran
School of Technology, Ilia State University, 0162 Tbilisi, Georgia
Authors to whom correspondence should be addressed.
Water 2019, 11(10), 2060;
Received: 23 August 2019 / Revised: 25 September 2019 / Accepted: 30 September 2019 / Published: 2 October 2019
Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data. View Full-Text
Keywords: Improved prediction; suspended sediment load; dynamic evolving neural-fuzzy inference system; DENFIS; ANFIS-FCM; MARS Improved prediction; suspended sediment load; dynamic evolving neural-fuzzy inference system; DENFIS; ANFIS-FCM; MARS
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Adnan, R.M.; Liang, Z.; El-Shafie, A.; Zounemat-Kermani, M.; Kisi, O. Prediction of Suspended Sediment Load Using Data-Driven Models. Water 2019, 11, 2060.

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