Probabilistic flood forecast for a small Pantanal watershed

  • Marcia Ferreira Cristaldo Instituto Federal de Mato Grosso do Sul (IFMS), Aquidauana, MS, Brasil Departamento de Computação.
  • Celso Correia de Souza Universidade Anhanguera (UNIDERP), Campo Grande, MS, Brasil Departamento de Estatística.
  • Leandro de Jesus Instituto Federal de Mato Grosso do Sul (IFMS), Aquidauana, MS, Brasil Departamento de Computação.
  • Paulo Tarso Sanches de Oliveira Universidade Federal de Mato Grosso do Sul (UFMS), Campo Grande, MS, Brasil Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia (FAENG).
  • Carlos Roberto Padovani Empresa Brasileira de Pesquisa Agropecuária (Embrapa), Corumbá, MS, Brasil Centro de Pesquisa Agropecuária do Pantanal. Departamento de Geociências.
  • Hevelyne Henn da Gama Viganó Instituto Federal de Mato Grosso do Sul (IFMS), Campo Grande, MS, Brasil Departamento de Estatística.
DOI: https://doi.org/10.4136/ambi-agua.1988
Keywords: Aquidauana River, multilayer perceptron, prediction, quota monitoring.

Abstract

Monitoring for flood forecasting in small hydrographic basins is of great importance in view of the relationship of water resources with society, as it can guarantee the sustainable use of urban communities in cities bordering the basin. The Aquidauana River, classified as a small basin, belongs to the Paraguay River basin and is an affluent of the Miranda River, forming part of the Pantanal plain, being inserted in the mapping of rivers vulnerable to flooding in the Central-West region of Brazil. This study deals with the monitoring of the river Aquidauana and uses artificial neural networks (RNAs) of the MultiLayer Perceptron type (training back-propagation) with parameters optimized by Genetic Algorithms. The RNA was trained and tested based on hydrological data between 1995 and 2014, accumulated rainfall (mm) and river level (cm) upstream. The forecast was 1 to 5 days, with the best performance of the model for 1 day of forecast, with a coefficient of determination and mean square error of 0.93 and 30 (cm), respectively.


Published
06/08/2018
Issue
Vol. 13 No. 4 (2018)
Section
Papers

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