Modelamiento de procesos hidrológicos aplicando técnicas de inteligencia artificial: una revisión sistemática de la literatura

The field of hydrology is one of the sciences that focuses on the study, planning and quantification of water resources, generating a significant amount of data, which are indispensable in the branch of civil engineering. Currently these data are analyzed by a variety of techniques, among the predom...

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Autores:
Rafael Miñope, Willians Franklin
Vilcherres Lizárraga, Pedro Victor Raúl
Muñoz Pérez, Sócrates Pedro
Tuesta Monteza, Victor Alexci
Mejía Cabrera, Heber Ivan
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/43649
Acceso en línea:
http://revistas.ustabuca.edu.co/index.php/ITECKNE/article/view/2645
Palabra clave:
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License
http://purl.org/coar/access_right/c_abf2
Description
Summary:The field of hydrology is one of the sciences that focuses on the study, planning and quantification of water resources, generating a significant amount of data, which are indispensable in the branch of civil engineering. Currently these data are analyzed by a variety of techniques, among the predominant ones are artificial intelligence (IA) exclusively applied to the modeling of hydrological processes such as rain-runoff, floods, droughts, evapotranspiration, lake level and flow prediction. This document carried out a systematic review of the literature published between the years 2015 to 2021 in the various databases such as Scopus, Springer Link, EBSCOhost, SciELO and ScienceDirect. For this, a protocol process was established in which the selected database, definition of search terms and selection filters are entered. Indeed, after considering the protocol process, 50 indexed articles were obtained in addition to 4 articles and 1 book of web pages. As a consequence, it was found that artificial neural networks (ANNs) are the most widely used techniques for modeling hydrological processes where, with innovative programming languages, they can be encoded with much greater versatility. To date, the use of RNA is being implemented with other techniques to generate hybrid models that allow obtaining better estimates.