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    A comparison of modeling techniques to predict hydrological indices in ungauged rivers

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    Identificadores
    URI: http://hdl.handle.net/10902/15892
    DOI: 10.23818/limn.37.12
    ISSN: 0213-8409
    ISSN: 1989-1806
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    Author
    Peñas Silva, Francisco Jesús; Barquín Ortiz, JoséAutoridad Unican; Álvarez Díaz, CésarAutoridad Unican
    Date
    2018
    Derechos
    © Asociación Ibérica de Limnología
    Publicado en
    Limnetica, 37 (1): 145-158(2018)
    Publisher
    Asociación Ibérica de Limnología
    Abstract:
    Predicting the natural flow regime in ungauged rivers is an important challenge in water resource management and ecological research. We developed models to predict 16 hydrological indices in a river network covering the northern third of the Iberian Peninsula. Multiple Linear Regression (MLR), Generalized Additive Models (GAMs), Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used and compared according to their prediction accuracy. The results showed that predictive performance varied greatly depending on the modeled hydrological attribute. The magnitude and frequency indices were predicted with excellent accuracy. In contrast, no technique was capable of developing precise models for hydrological indices of timing, duration and rate of change. This is mainly related to the lack of proper environmental databases on the scales on which these flow regime patterns are influenced. In addition, complex modeling techniques did not always outperform linear models and no single approach was optimal for all indices. ANFIS and GAMs provided the best results; however, other issues such as computational cost and the level of knowledge required to apply the method and interpret the results should be taken into account.
    Collections to which it belong
    • D56 Artículos [180]
    • D56 Proyectos de Investigación [103]

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    UNIVERSIDAD DE CANTABRIA

    Repositorio realizado por la Biblioteca Universitaria utilizando DSpace software
    Contact Us | Send Feedback
    Metadatos sujetos a:licencia de Creative Commons Reconocimiento 3.0 España