Abstract
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.
Highlights
Modelling sediment transport accurately is of great importance tool in many fields of water resources management such as water quality, reservoir management, designing of dam and operation
The objectives of this study is to investigate the capability of two types of artificial neural networks
It can be concluded that the Adaptive Neuro Fuzzy Inference System (ANFIS) model outperform the other two evaluated methods. this could be attributed to the fact that the ANFIS model combines the linguistic representation of a fuzzy system with the learning ability of the artificial Neural Network (ANN) [20]
Summary
Modelling sediment transport accurately is of great importance tool in many fields of water resources management such as water quality, reservoir management, designing of dam and operation. It is of great importance to estimate the inflow amounts of sediment in water systems Artificial intelligence approaches such as artificial Neural Network (ANN), support vector regression, fuzzy logic (FL) are used in prediction of suspended sediment [2] as they provide a good alternative to conventional approaches like linear or deterministic models. Artificial neural network (ANN) is a tremendously parallel- distributed information processing system, which is obtained from research on the nature of human brains [3]. Many advantages of this technique and in particular in the field of hydrology were proved. Many successful applications of ANN’s in the field of sediment transport were reported [6,7,8,9,10,11,12,13,14,15]
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