Comparison between inverse modelling and data assimilation to estimate rainfall from runoff using the multilayer perceptron

Abstract

The ability of the multilayer perceptron to model the inverse relation of a fictitious watershed is investigated. Comparison is done between a new formulation of data assimilation and the standard multilayer perceptron applied to three kinds of models: static, feedforward and recurrent. It appears that both techniques are equivalent and allow a very good estimation of the inverse relation. This study aims at proposing methods to supplement or adapt historical databases to modern instrumentation. Datasets will thus be used over a longer time-series to better apprehend the consequences of global warming.

DOI: 10.1109/IJCNN.2015.7280427

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Cite this paper

@article{Johannet2015ComparisonBI, title={Comparison between inverse modelling and data assimilation to estimate rainfall from runoff using the multilayer perceptron}, author={Anne Johannet and Virgile Taver and Marc Vinches and Valerie Borrell Estupina and Severin Pistre and Dominique Bertin}, journal={2015 International Joint Conference on Neural Networks (IJCNN)}, year={2015}, pages={1-8} }