@Article{SantosFrBaSoDiLiSt:2023:NeNeHy,
author = "Santos, Leonardo Bacelar Lima and Freitas, Cintia Pereira de and
Bacelar, Luiz and Soares, Jaqueline Aparecida Jorge Papini and
Diniz, Michael M. and Lima, Glauston R. T. and Stephany, Stephan",
affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais
(CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Duke University} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and Instituto Federal de Educa{\c{c}}{\~a}o,
Ci{\^e}ncia e Tecnologia de S{\~a}o Paulo (IFSP) and {Centro
Nacional de Monitoramento e Alertas de Desastres Naturais
(CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "A Neural Network-Based Hydrological Model for Very High-Resolution
Forecasting Using Weather Radar Data",
journal = "Eng",
year = "2023",
volume = "4",
number = "3",
pages = "1787--1796",
month = "Sept.",
keywords = "hydrologic prediction, hydrological prediction, hydrology, neural
networks, weather radar.",
abstract = "Many hydro-meteorological disasters in small and steep watersheds
develop quickly and significantly impact human lives and
infrastructures. High-resolution rainfall data and machine
learning methods have been used as modeling frameworks to predict
those events, such as flash floods. However, a critical question
remains: How long must the rainfall input data be for an
empirical-based hydrological forecast? The present article
employed an artificial neural network (ANN)hydrological model to
address this issue to predict river levels and investigate its
dependency on antecedent rainfall conditions. The tests were
performed using observed water level data and high-resolution
weather radar rainfall estimation over a small watershed in the
mountainous region of Rio de Janeiro, Brazil. As a result, the
forecast water level time series only archived a successful
performance (i.e., NashSutcliffe model efficiency coefficient
(NSE) > 0.6) when data inputs considered at least 2 h of
accumulated rainfall, suggesting a strong physical association to
the watershed time of concentration. Under extended periods of
accumulated rainfall (>12 h), the framework reached considerably
higher performance levels (i.e., NSE > 0.85), which may be related
to the ability of the ANN to capture the subsurface response as
well as past soil moisture states in the watershed. Additionally,
we investigated the models robustness, considering different seeds
for random number generating, and spacial applicability, looking
at maps of weights.",
doi = "10.3390/eng4030101",
url = "http://dx.doi.org/10.3390/eng4030101",
issn = "2673-4117",
language = "en",
targetfile = "eng-04-00101-v2.pdf",
urlaccessdate = "21 maio 2024"
}