The map-correlation method for ungauged catchments streamflow prediction in the Ufa River, Russian Federation

Krasnogorskaya, N. N.; Belozerova, Elena; Longobardi, Antonia; Nafikova, Elvira

doi:10.1504/ijhst.2019.103440

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“…In some works [11][12][13], attempts have been made to combine genetic algorithms (GA) and ANN, the possibilities of using GA to optimize the learning process of ANN, aimed at reducing the amount of computations, have been investigated, provided that the accuracy of the solution is maintained at the required level.…”

Section: Introductionmentioning

confidence: 99%

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Hydroecological data recovery using artificial intelligence

Nafikova¹,

Aleksandrov²,

Shaniyazova³

et al. 2023

E3S Web of Conf.

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Artificial neural networks and genetic algorithms have been tested for the restoration of missed hydroecological indicators (hydrological water quality parameters and bottom sediment quality parameters). Algorithms have been developed for recovering missing hydroecological data in the presence and absence of observation data from points upstream and downstream. Neural network models for the restoration of water quality indicators and bottom sediments were tested on the example of the catchment basin of the Bayda and Kidysh rivers in the territory of the Republic of Bashkortostan.

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Section: Introductionmentioning

confidence: 99%

“…The analysis of works [11][12][13] showed that for the restoration of missing data of natural processes it is most preferable to use the ANN of the following structures: multilayer perceptron (MLP), radially basic networks (RBF), generalized regression networks (GRNN).…”

Section: Introductionmentioning

confidence: 99%