Bibliographic citations
Mendoza, C., (2020). Desarrollo de un modelo hidrológico semidistribuido a nivel diario mediante redes neuronales en cuencas de la costa peruana [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/21238
Mendoza, C., Desarrollo de un modelo hidrológico semidistribuido a nivel diario mediante redes neuronales en cuencas de la costa peruana [Tesis]. PE: Universidad Nacional de Ingeniería; 2020. http://hdl.handle.net/20.500.14076/21238
@misc{renati/1750054,
title = "Desarrollo de un modelo hidrológico semidistribuido a nivel diario mediante redes neuronales en cuencas de la costa peruana",
author = "Mendoza Mendoza, Carlos Javier",
publisher = "Universidad Nacional de Ingeniería",
year = "2020"
}
The lack of hydrometeorological information present in Peru, as well as the complexity of the nature of the basin, makes it a challenge for engineers who are engaged in this area of engineering. In order to solve this problem, hydrological models are applied, however, the reality is that these models, for the most part, are not developed in Peru, so their applicability is limited and with a certain degree of uncertainty. It is in this context that the present thesis is developed, where it is sought to evaluate the neural networks as a tool capable of providing a model that contemplates the Peruvian reality. For the development of the thesis, the availability of hydrometeorological information was taken into account, which are daily flow, precipitation and temperature, soil type and topography, within the Peruvian coast area, identifying three sub-basins of the Piura River, a sub-basin of the Chillón river and one of the Fortaleza river. The model to be developed is of the semi-distributed type, so the Hydrological Response Units were defined for each of the sub-basins, according to the type of soil and the slope direction. On the other hand, the neural networks to be evaluated are pre-fed neural networks and NARX-type networks, in their two versions, due to their ability to solve regression-type problems. Specifically, it is sought not only to evaluate the applicability of ANNs, but also a group of their characteristics, the influence of the type of network, the architecture, the input data, the delay time and the time of learning, in addition to comparing ANNs with the GR4J model. The results show that NARX type networks, which consider the flow of previous days as an input, have the best performance according to the NSE indicator, with values that do not fall below 0.6 in the test tests, and in calibration It does not fall below 0.8, however, when it is evaluated in the simulation of extraordinary events such as the case of the ENSO of 83, it is observed that, according to the methodology used, the network is not able to represent the flow rate on a daily scale, but if On a monthly scale. On the other hand, of the type of input data, ANN is capable of dispensing with temperature and evapotranspiration, generated from temperature. In comparison with the GR4J model, the ANN exceeds this model according to the NSE, on the other hand, in the simulation of extraordinary events, this difference does not exist on a daily scale, but for a monthly scale.
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