Modelling Cyanbacteria (blue-green algae) in the River Murray using artificial neural networks
Mathematics and Computers in Simulation - Special issue: selection of papers presented at the MSSA/IMACS 11th biennial conference on modelling and simulation, Newcastle, New South Wales, Australia, November 1995
Greenhouse temperature modeling: a comparison between sigmoid neural networks and hybrid models
Mathematics and Computers in Simulation - Special issue: Selected papers of the IMACS/IFAC fourth international symposium on mathematical modelling and simulation in agricultural and bio-industries
Mathematics and Computers in Simulation - Special issue: Mathematical modeling of ecological systems
River flow estimation using adaptive neuro fuzzy inference system
Mathematics and Computers in Simulation
Prediction of rainfall time series using modular soft computingmethods
Engineering Applications of Artificial Intelligence
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Artificial neural network (ANN) models are designed for suspended sediment estimation using statistical pre-processing of the data. Statistical properties such as cross-, auto- and partial auto-correlation of the data series are used for identifying a unique input vector to the ANN that best represents the sediment estimation process for a basin. The methodology is evaluated using the flow and sediment data from the stations Quebrada Blanca and Rio Valenciano in USA. The result of the study indicates that the statistical pre-processing of the data could significantly reduce the effort and computational time required in developing an ANN model. Three ANN training algorithms are also compared with each other for the selected input vector.