Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan, Rana Muhammad and Liang, Zhongmin and El-Shafie, Ahmed and Zounemat-Kermani, Mohammad and Kisi, Ozgur (2019) Prediction of Suspended Sediment Load Using Data-Driven Models. Water, 11 (10). p. 2060. ISSN 2073-4441, DOI

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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. © 2019 by the authors.

Item Type: Article
Funders: National Natural Science Foundation of China (41730750)
Uncontrolled Keywords: Improved prediction; suspended sediment load; dynamic evolving neural-fuzzy inference system; DENFIS; ANFIS-FCM; MARS
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 18 May 2020 04:51
Last Modified: 18 May 2020 04:51

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