@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 = "16 abr. 2021"
}