Artificial intelligence for suspended sediment load prediction: a review

2021 ◽  
Vol 80 (9) ◽  
Author(s):  
Deepak Gupta ◽  
Barenya Bikash Hazarika ◽  
Mohanadhas Berlin ◽  
Usha Mary Sharma ◽  
Kshitij Mishra
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Load Prediction

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

2020 ◽  
Vol 65 (12) ◽  
pp. 2116-2127
Author(s):  
Viet-Ha Nhu ◽  
Khabat Khosravi ◽  
James R. Cooper ◽  
Mahshid Karimi ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Intelligence Testing ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Random Subspace ◽  
Subspace Method ◽  
Load Prediction ◽  
Random Subspace Method

scholarly journals Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

2018 ◽  
Vol 162 ◽  
pp. 03003 ◽  
Author(s):  
Mustafa Al-Mukhtar
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Tigris River ◽  
Artificial Neural Network Ann

Modeling of suspended sediment load in rivers has a major role in a proper management of water resources. Artificial intelligence has been identified as an efficient way to model the complex nonlinear hydrological relationship. In this study, Adaptive Neuro Fuzzy Inference System (ANFIS), in addition to two different kinds of Artificial Neural Network (ANN) i.e. feedforward and radial basis networks were used and compared to model the suspended sediment load (SSL) in Tigris River-Baghdad using the streamflow discharge as input. To this end, an intermittent data of SSL and streamflow were collected over the period 1962-1981 from Sarai station in Baghdad. 70 % of these data was used to calibrate (train) the networks and the remaining 30% for the validation (test). The coefficient of determination (R2), root mean square error (RMSE), and Nash and Sutcliffe model efficiency coefficient (NSE) were used to judge whether the observed and modelled data belong to the same distribution. Results revealed that the ANFIS model outperform the other methods. R2, RMSE, and NSE of ANFIS during the calibration phase were equal to 0.58, 75617, and 0.58, respectively and during the validation were 0.72, 27944, and 0.59, respectively. Therefore, ANFIS approach is recommended to estimate the river suspended sediment load.

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