%0 Journal Article
%4 sid.inpe.br/mtc-m21d/2023/08.11.19.42
%2 sid.inpe.br/mtc-m21d/2023/08.11.19.42.44
%@doi 10.3390/eng4030101
%@issn 2673-4117
%T A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data
%D 2023
%8 Sept.
%9 journal article
%A Santos, Leonardo Bacelar Lima,
%A Freitas, Cintia Pereira de,
%A Bacelar, Luiz,
%A Soares, Jaqueline Aparecida Jorge Papini,
%A Diniz, Michael M.,
%A Lima, Glauston R. T.,
%A Stephany, Stephan,
%@affiliation Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Duke University
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
%@affiliation Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress leonardo.santos@cemaden.gov.br
%@electronicmailaddress cintia.cpfreitas@gmail.com
%@electronicmailaddress luiz.bacelar@duke.edu
%@electronicmailaddress jaqueline.soares@cemaden.gov.br
%@electronicmailaddress michael.diniz@ifsp.edu.br
%@electronicmailaddress glauston.lima@cemaden.gov.br
%@electronicmailaddress stephan.stephany@inpe.br
%B Eng
%V 4
%N 3
%P 1787-1796
%K hydrologic prediction, hydrological prediction, hydrology, neural networks, weather radar.
%X 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.
%@language en
%3 eng-04-00101-v2.pdf