dc.creator | Zavala, Gustavo R. | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Nebro, Antonio J. | es |
dc.date.accessioned | 2021-05-12T11:24:46Z | |
dc.date.available | 2021-05-12T11:24:46Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Zavala, G.R., García Nieto, J.M. y Nebro, A.J. (2019). Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization. Applied Sciencies, 10 (1), 251-1-251-28. | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108931 | |
dc.description.abstract | The efficient calibration of hydrologic models allows experts to evaluate past events in
river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty
in this context is the need to adjust a large number of parameters in the model to reduce prediction
errors. In this work, we address this issue with two complementary contributions. First, we propose
a new lumped rainfall-runoff hydrologic model—called Qom—which is featured by a limited set of
continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate
and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin,
while a Clark’s model is used to determine output hydrograms. Second, we apply a multi-objective
optimization approach to find accurate calibrations of the model in a systematic and automatic way.
The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe
Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set
of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic
scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed
approach is shown to minimize prediction errors of simulated streamflows with regards to those
observed in these real-world basins. | es |
dc.description.sponsorship | Ministerio de Educación y Ciencia TIN2017-86049-R | es |
dc.format | application/pdf | es |
dc.format.extent | 28 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciencies, 10 (1), 251-1-251-28. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hydrologic model | es |
dc.subject | Prediction | es |
dc.subject | Multi-objective optimization | es |
dc.subject | Metaheuristics | es |
dc.title | Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2017-86049-R | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/10/1/251 | es |
dc.identifier.doi | 10.3390/app10010251 | es |
dc.journaltitle | Applied Sciencies | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 251-1 | es |
dc.publication.endPage | 251-28 | es |
dc.contributor.funder | Ministerio de Educación y Ciencia (MEC). España | es |