Level river forecasting using empirical hydrological modeling for Rio Negro basin Uruguay

Johan S. Duque, Leonardo do Santos, Rafael Santos, Johny A. Arteaga, Natalie Aubet


Climate change has influenced several of the water cycle related variables such as  rainfall that contribute to increasing natural disasters. To establish new methodologies for rivers level forecasting is necessary for the implementation of early warning systems. In this work, we present results of a multilayer perceptron artificial neural network (ANN) to forecast temporal series of water levels at the outlet of Rio Negro river with 24-hour antecedence. Input data was collected by a set of hydrological monitoring stations composed of water level and rainfall measures acquired with a one-day resolution. Water-level prediction were evaluated by the Nash-Sutcliffe coefficient (NSE) and by the root mean square error (RMSE). The results show consistency between predicted and observed values, especially when combining both water level and rainfall data. In such case, values of NSE reached 0.93 to 0.54 and RMSE between 0.028 and 0.061 for antecedence of 1 to 7 days respectively with implemented topology for the empirical model.


Empirical hydrological modeling; Water-level; Rain; Neural networks.

Texto completo:

PDF (English)


IPCC Fourth Assessment Report. Website Accessed 10/12/2021. In: https : / / archive . ipcc.ch/publications_and_data/ar4/wg2/en/ch3s3-4-3.html (2007).

UTE official website. Accessed 06/07/2020. In: https://portal.ute.com.uy/precipitacionesocurridas-y-prevision-de-niveles (). 7

C. W. Dawson and R. L. Wilby. “Hydrological modelling using artificial neural networks.” In: Progress in Physical Geography 25.1 (2001), pp. 80–108.

Editorial. “The rise of data-driven modelling”. In: Nat Rev Phys. https://doi.org/10. 1038/s42254-021-00336-z (2021), p. 383.

C. Pereira de Freitas. “Combining Rainfall and Water Level Data for Multistep High Temporal Resolution Empirical Hydrological Forecasting”. In: (2020).

M Jajarmizadeh and S Harun. “A Review on Theoretical Consideration and Types of Models in Hydrology”. In: Journal of Environmental Science and Technology (2012), pp. 249– 261.

Hornik K, Stinchcombe M, and H. White. “Multilayer feedforward networks are universal approximators”. In: Neural Networks. doi : 10 . 1016 / 0893 - 6080(89 ) 90020 - 8 (1989), pp. 359–366.

H.C. Daily Kilinc. “Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin”. In: Water. https://doi. org/10.3390/w14030490 (2022), pp. 14–490.

T. Kokkonen, H. Koivusalo, and T. Karvonen. “A semi-distributed approach to rainfall- runoff modelling—a case study in a snow affected catchment.” In: Environmental Modelling and Software, 16(5). (2001), pp. 481–493.

Online site. Ministerio de Transporte y Obras Publicas. Accessed 31/08/2020. https : / / www.gub.uy/ministerio- transporte- obras- publicas/institucional/informaciongestion/pedidos-informes/inundaciones-ciudad-mercedes-soriano.

A. Mosavi, P. Ozturk, and K.W. Chau. “Flood prediction using machine learning models: literature review”. In: Water 10.11 (2018).

Website repository. In: https://github.com/JD39/Empirical-hidrological-forecasting. git (2021).

Zhu. S et al. “An improved long short-term memory network for streamflow forecasting in the upper Yangtze River”. In: Stoch Environ Res Risk Assess, 34 (2020), pp. 1313–1329.

M. Sadres. “Desarrollo de un modelo en la cuenca alta del Río Negro (Uruguay) utilizando Hydrobid: Análisis de la Disponibilidad de Agua en Diferentes Escenarios”. In: Tesis de Maestría, Universidad de Alcalá. 2019.

L.B.L Santos et al. “An operational dynamical neuro-forecasting model for hydrological disasters”. In: Modeling Earth Systems and Environment, (2001), p. 94.

Sistema Nacional de Emergencias. Online. Accessed 01/03/2020. https://www. gub.uy/sistema-nacional-emergencias/comunicacion/noticias/eventos-adversossignificativos-ocurridos-uruguay-entre-anos-2015-2019.

Chahine M. T. “The Hydrological Cycle and Its Influence on Climate”. In: Nature (1992), pp. 373–380.

Weinan et al. “Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don’t”. In: https://arxiv.org/pdf/2009.10713.pdf (2020), pp. 1–56

DOI: https://doi.org/10.5540/03.2022.009.01.0269


  • Não há apontamentos.

SBMAC - Sociedade de Matemática Aplicada e Computacional
Edifício Medical Center - Rua Maestro João Seppe, nº. 900, 16º. andar - Sala 163 | São Carlos/SP - CEP: 13561-120

Normas para publicação | Contato