A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Author:

Santos Leonardo B. L.12,Freitas Cintia P.2,Bacelar Luiz3,Soares Jaqueline A. J. P.12,Diniz Michael M.4ORCID,Lima Glauston R. T.1,Stephany Stephan2ORCID

Affiliation:

1. Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos 12630-000, Brazil

2. National Institute for Space Research (INPE), São José dos Campos 12227-010, Brazil

3. Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA

4. Federal Institute of São Paulo (IFSP-SP), São José dos Campos 12223-201, Brazil

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., Nash–Sutcliffe 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 model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Funder

CNPq

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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