Multiobjective training of artificial neural networks for rainfall-runoff modeling
نویسندگان
چکیده
منابع مشابه
Artificial neural networks as rainfall- runoff models
A series of numerical experiments, in which flow data were generated from synthetic storm sequences routed through a conceptual hydrological model consisting of a single nonlinear reservoir, has demonstrated the closeness of fit that can be achieved to such data sets using Artificial Neural Networks (ANNs). The application of different standardization factors to both training and verification s...
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Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where the available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporati...
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The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A l...
متن کاملRainfall - Runoff Modelling Using Artificial Neural Networks ( ANNs )
Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...
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This paper proposes a hybrid intelligent model for runoff prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and levenberg–marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as dat...
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ژورنال
عنوان ژورنال: Water Resources Research
سال: 2008
ISSN: 0043-1397
DOI: 10.1029/2007wr006734