Modeling of hydrological processes applying artificial intelligence techniques: a systematic literature review
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.
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