estimating river suspended sediment yield using mlp neural network in arid and semi-arid basins case study: bar river, neyshaboor, iran

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.