MODELING TOTAL SEDIMENT LOAD IN RIVERS USING ARTIFICIAL NEURAL NETWORKS

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FALAMAKI AMIN; ESKANDARI MAHNAZ; BAGHLANI ABDOLHOSSEIN; AHMADI SEYED AHMAD

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Journal: JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION Year:2013 | Volume:2 | Issue:3 Page(s): 0-0

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Title

MODELING TOTAL SEDIMENT LOAD IN RIVERS USING ARTIFICIAL NEURAL NETWORKS

Author(s)

FALAMAKI AMIN | ESKANDARI MAHNAZ | BAGHLANI ABDOLHOSSEIN | AHMADI SEYED AHMAD | Issue Writer Certificate

Keywords

MULTI LAYER PERCEPTRONEQ2

RADIAL BASIS FUNCTIONSQ1

SEDIMENT TRANSPORT FORMULASQ1

TOTAL SEDIMENT CONCENTRATIONQ2

Abstract

Estimating total sediment load in rivers is an important and practical issue for water resources planning and management. The sediment concentration can be calculated by both direct and indirect measurements, but direct methods are usually costly and time-consuming. Further, total sediment load can be determined by several sediment load transport models. These equations, however, are applicable in certain circumstances, and in most cases the outcomes do not agree with each other and with measured data. The objective of this study was to propose a method based on artificial neural networks (ANN) to predict total sediment load concentration. Consequently, two ANNs including multilayer perceptrone (MLP) and radial basis function (RBF) with 200 data were used for the modeling purposes. For training and testing the ANN models, 75 and 25 percent of data were used, respectively. The input variables were designated to be average flow velocity, average depth, water surface slope, canal width and median particle diameter of sediment, while the models output was total sediment load concentration. The input variables were included to the models step wisely and the results were evaluated to find out the most suitable ANN models. The predicted values were then compared with five known sediment load transport equations. The conducted statistical analyses indicated that ANNs models in particular MLP can provide better prediction for total sediment load with correlation coefficient of 0.96. It was further concluded that to enhance the accuracy of ANN model, training of the network should be accomplished using both hydrological and sediment data. The Ackers and White equation was very overestimating the total sediment load, while all other equations were underestimating. Based on the results obtained in this study, the ANN-based models provide better concurrence with the observed data, particularly MLP network which can reasonably well predict the peak point of total sediment.

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APA: Copy

FALAMAKI, AMIN, ESKANDARI, MAHNAZ, BAGHLANI, ABDOLHOSSEIN, & AHMADI, SEYED AHMAD. (2013). MODELING TOTAL SEDIMENT LOAD IN RIVERS USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, 2(3), 0-0. SID. https://sid.ir/paper/403921/en

Vancouver: Copy

FALAMAKI AMIN, ESKANDARI MAHNAZ, BAGHLANI ABDOLHOSSEIN, AHMADI SEYED AHMAD. MODELING TOTAL SEDIMENT LOAD IN RIVERS USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION[Internet]. 2013;2(3):0-0. Available from: https://sid.ir/paper/403921/en

IEEE: Copy

AMIN FALAMAKI, MAHNAZ ESKANDARI, ABDOLHOSSEIN BAGHLANI, and SEYED AHMAD AHMADI, “MODELING TOTAL SEDIMENT LOAD IN RIVERS USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, vol. 2, no. 3, pp. 0–0, 2013, [Online]. Available: https://sid.ir/paper/403921/en

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