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@Article{CassalhoBeMeMoOlAg:2019:StReFl,
               author = "Cassalho, Felicio and Beskow, Samuel and Mello, Carlos 
                         Rog{\'e}rio de and Moura, Ma{\'{\i}}ra Martim de and Oliveira, 
                         Leroi Floriano and Aguiar, Marilton Sanchotene de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Pelotas (UFPEL)} and {Universidade 
                         Federal de Lavras (UFLA)} and {Universidade Federal de Pelotas 
                         (UFPEL)} and {Universidade Federal de Pelotas (UFPEL)} and 
                         {Universidade Federal de Pelotas (UFPEL)}",
                title = "Artificial intelligence for identifying hydrologically homogeneous 
                         regions: A state-of-the-art regional flood frequency analysis",
              journal = "Hydrological Processes",
                 year = "2019",
               volume = "33",
               number = "7",
                pages = "1101--1116",
                month = "mar.",
             keywords = "cluster analysis, evolutionary computation, fuzzy logic, 
                         heterogeneity measure, index-flood, L-moments.",
             abstract = "Due to the severity related to extreme flood events, recent 
                         efforts have focused on the development of reliable methods for 
                         design flood estimation. Historical streamflow series correspond 
                         to the most reliable information source for such estimation; 
                         however, they have temporal and spatial limitations that may be 
                         minimized by means of regional flood frequency analysis (RFFA). 
                         Several studies have emphasized that the identification of 
                         hydrologically homogeneous regions is the most important and 
                         challenging step in an RFFA. This study aims to identify 
                         state-of-the-art clustering techniques (e.g., K-means, partition 
                         around medoids, fuzzy C-means, K-harmonic means, and genetic 
                         K-means) with potential to form hydrologically homogeneous regions 
                         for flood regionalization in Southern Brazil. The applicability of 
                         some probability density function, such as generalized extreme 
                         value, generalized logistic, generalized normal, and Pearson type 
                         3, was evaluated based on the regions formed. Among all the 15 
                         possible combinations of the aforementioned clustering techniques 
                         and the Euclidian, Mahalanobis, and Manhattan distance measures, 
                         the five best were selected. Several watersheds' physiographic and 
                         climatological attributes were chosen to derive multiple 
                         regression equations for all the combinations. The accuracy of the 
                         equations was quantified with respect to adjusted coefficient of 
                         determination, root mean square error, and Nash-Sutcliffe 
                         coefficient, whereas, a cross-validation procedure was applied to 
                         check their reliability. It was concluded that reliable results 
                         were obtained when using robust clustering techniques based on 
                         fuzzy logic (e.g., K-harmonic means), which have not been commonly 
                         used in RFFA. Furthermore, the probability density functions were 
                         capable of representing the regional annual maximum streamflows. 
                         Drainage area, main river length, and mean altitude of the 
                         watershed were the most recurrent attributes for modelling of mean 
                         annual maximum streamflow. Finally, an integration of all the five 
                         best combinations stands out as a robust, reliable, and simple 
                         tool for estimation of design floods.",
                  doi = "10.1002/hyp.13388",
                  url = "http://dx.doi.org/10.1002/hyp.13388",
                 issn = "0885-6087",
             language = "en",
           targetfile = "Cassalho_et_al-2019-Hydrological_Processes.pdf",
        urlaccessdate = "27 nov. 2020"
}


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