TY - JOUR ID - 21746 TI - Estimating river suspended sediment yield using MLP neural network in arid and semi-arid basins Case study: Bar River, Neyshaboor, Iran JO - Desert JA - JDESERT LA - en SN - 2008-0875 AU - Memarian Khalilabad, H. AU - Feiznia, S. AU - Zakikhani, K. AD - Expert of Abkhizgostar-e-Shargh Consulting Engineers Co., Mashhad, Iran AD - Professor, Faculty of Natural Resources, University of Tehran AD - Student, College of Civil Engineering, University of Tehran Y1 - 2009 PY - 2009 VL - 14 IS - 1 SP - 43 EP - 52 KW - Arid and semi-arid basins KW - MLP Neural Network KW - perceptron KW - Suspended sediment yield DO - 10.22059/jdesert.2010.21746 N2 - Abstract Erosion and sedimentation are the most complicated problems in hydrodynamic which are very important in water-related projects of arid and semi-arid basins. For this reason, the presence of suitable methods for good estimation of suspended sediment load of rivers is very valuable. Solving hydrodynamic equations related to these phenomenons and access to a mathematical-conceptual model is very difficult and in most cases, necessary data for these models are not available. On the other hand, most of the widely-used experimental methods are not accurate-enough. The principles of wise method are: using the hidden knowledge in the data; effort to extract intrinsic relations between data; and generalizing them to other situations. Artificial neural network is one of the most important methods of artificial intelligence in which by inspiring from the model of human brain while performing training process, data-related information are stored into weights of network. The aim of this research is using MLP (Multi-Layer Perceptron) neural network to obtain sediment rating curve. After entering input patterns into the network and defining a neuron for input and a neuron for output layers and performing repeated trial and error, optimum architecture (topology) of MLP network was defined as a network with 5 neuron for hidden layers and Hyperbolic tangent activation function for the first and second hidden layers and Linear function for the third hidden layer. UR - https://jdesert.ut.ac.ir/article_21746.html L1 - https://jdesert.ut.ac.ir/article_21746_0f2c705b9ce09342fdb8820dd651c3c1.pdf ER -