Daily river level forecast based on the development of an artificial neural network: case study in La Virginia - Risaralda
DOI:
https://doi.org/10.17533/udea.redin.n76a06Keywords:
flood forecasting, flood risk, artificial neural networksAbstract
The municipality of La Virginia (Risaralda, Colombia) is constantly affected by fl oods that originate from increased water levels in the Cauca River. Disaster relief agencies do not currently have adequate monitoring systems to identify potential overfl ow events in time-series observations to prevent fl ood damage to homes or injury to the general population. In this paper, various simulation models are proposed for the prediction of fl ooding that contributes as a technical tool to the development and implementation of early warning systems to improve the responsiveness of disaster relief agencies. The models, which are based on artifi cial neural networks, take hydroclimatological information from different stations along the Cauca River Basin, and the trend indicates the average daily level of the river within the next 48 hours. This methodology can be easily applied to other urban areas exposed to fl ood risks in developing countries.
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J. Pulecio. Mitigación de inundaciones ocasionadas por los ríos Risaralda y Cauca en el Municipio de La Virginia, Departamento de Risaralda. Informe final de Consultoría a la Corporación Autónoma Regional de Risaralda (CARDER). Pereira, Colombia. 2010. pp. 1-24.
H. Vásquez, M. Holguín, G. Osorio. Base Ambiental con Énfasis en Riesgos. Municipio de La Virginia. Documento técnico de la Corporación Autónoma Regional del Risaralda -CARDER-. Pereira, Colombia. 2002. pp. 1-104.
Alcaldía de La Virginia - Risaralda. Nuestro Municipio: Geografía. Available on: http://www.lavirginia-risaralda.gov.co/informacion_general.shtml. Accessed: March 22, 2011.
Departamento Administrativo Nacional de Estadística (DANE). Boletín Censo General 2005. Perfil La Virginia, Risaralda. DANE. Bogotá, Colombia. 2010. pp. 1-6.
Dirección de Gestión de Riesgos (DGR), Corporación OSSO. Disaster Information System – DESINVENTAR. Available on: http://www.desinventar.net/DesInventar/main.jsp?countrycode=col. Accessed: May 15, 2011.
Comité Local para la Prevención y Atención de Desastres del Municipio de La Virginia (CLOPAD). Plan Local de Emergencia y Contingencia Municipio de La Virginia. CLOPAD. La Virginia, Colombia. 2009. pp. 1-151.
Corporación Autónoma Regional del Risaralda (CARDER). Diagnóstico de Riesgos Ambientales, Municipio de La Virginia. CARDER. Pereira, Colombia. 2002. pp. 1-50.
Consorcio Evaluación de Riesgos Naturales (ERN). Metodología de evaluación probabilista de riesgos naturales. Central American Probabilistic Risk Assessment (CAPRA). Available on: http://www.ecapra.org/es/metodolog%C3%ADa-de-evaluaci%C3%B3nprobabilista-de-riesgos-naturales. Accessed: February 26, 2011.
D. Matich. Redes Neuronales: Conceptos Básicos y Aplicaciones. Informe. Universidad Tecnológica Nacional. Rosario, Argentina. 2001. Available on: http:// www.scribd.com/doc/48697993/Virus-Hack-RedesNeuronales-Conceptos-Basicos-y-Aplicaciones. Accessed: March 3, 2011.
E. Caicedo, J. López. Una aproximación práctica a las redes Neuronales Artificiales. 1st ed. Ed. Programa Editorial Universidad del Valle. Cali, Colombia. 2009. pp. 1-217.
R. González, X. Vargas, A. López, L. Belmar. Sistema de alerta de crecidas en el río Biobío. Uso recursivo de un modelo de Redes Neuronales. Proceedings of the III Encuentro de Aguas. Santiago, Chile. 2002. Available on: http://www.bvsde.paho.org/bvsacd/encuen/gonza. pdf. Accessed: March 14, 2011.
C. Guerreño, O. Ferreira, L. Chamorro, R. Almirón. “Análisis de Modelos Matemáticos Aplicados al Estudio del Río Paraguay”. Revista de Ciencia y Tecnología. Vol. 1. 2001. pp. 111-118.
S. Hag. “Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan”. Alexandria Engineering Journal. Vol. 53. 2014. pp. 655-662.
W. Chih. “Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions”. Environmental Modelling & Software. Vol. 63. 2015. pp. 137-155.
B. Unal, M. Mamak, G. Seckin, M. Cobaner. “Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels”. Advances in Engineering Software. Vol. 41. 2010. pp. 120-129.
C. Wei, L. Wen, H. Ming. “Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage”. Advances in Engineering Software. Vol. 45. 2012. pp. 69-79.
B. Balouchi, M. Nikoo, J. Adamowski. “Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: Application of ANNs and the M5P model tree”. Applied Soft Computing Journal. Vol. 34. 2015. pp. 51-59.
H. Badrzadeh, R. Sarukkalige, A. Jayawardena. “Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting”. Journal of Hydrology. Vol. 507. 2013. pp. 75-85.
F. Chang, P. Chen, Y. Lu, E. Huang, K. Chang. “Realtime multi-step-ahead water level forecasting by recurrent neural networks for urban flood control”. Journal of Hydrology. Vol. 517. 2014. pp. 836-846.
G. Ovando, M. Bocco, S. Sayago. “Redes Neuronales para Modelar la Predicción de Heladas”. Revista Agricultura Técnica. Vol. 65. 2005. pp. 65-73.
Universidad del Valle, Corporación Autónoma Regional del Valle del Cauca (CVC). Volumen I: Caracterización del Río Cauca Tramo Salvajina – La Virginia. Available on: http://www.cvc.gov.co/cvc/Mosaic/Publicacionesf1.html#volumen1. Accessed: April 12, 2011.
Universidad del Valle, Corporación Autónoma Regional del Valle del Cauca (CVC). Volumen IV y V: Caracterización de Ríos Tributarios del Rio Cauca Tramo Salvajina – La Virginia. Available on: http://www.cvc.gov.co/cvc/Mosaic/Publicacionesf1.html#volumen4. Accessed: April 3, 2011.
M. Gestal. Introducción a las Redes de Neuronas Artificiales. Universidade da Coruña. http://sabia.tic.udc.es/mgestal/cv/RNAtutorial/TutorialRNA.pdf. Accessed: March 20, 2011.
F. Sánchez. Medidas Puntuales de Permeabilidad. Universidad de Salamanca. Available on: http://hidrologia.usal.es/temas/Slug_tests.pdf. Accessed: March 20, 2011.
M. Wanielista, R. Kersten, R. Eaglin. Hydrology: water quantity and quality control. 2nd ed. Ed. John Wiley & Sons. New York, USA. 1997. pp. 1-592.
M. Bruen, J. Yang. “Functional networks in real-time flood forecasting -a novel application”. Advances in Water Resources. Vol. 28. 2005. pp. 899-909.
L. Feng, J. Lu. “The practical research on flood forecasting based on artificial neural networks”. Expert Systems with Applications. Vol. 37. 2010. pp. 2974-2977.
J. García, A. López, J. Romero, A. García, C. Camacho, J. Cantero, et. al. “Hojas de cálculo para la simulación de redes de neuronas artificiales (RNA)”. Questiió. Vol. 26. 2002. pp. 289-305.
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