How a Computational Method Can Help to Improve the Quality of River Flood Prediction by Simulation

Adriana Gaudiani (), Emilio Luque, Pablo García, Mariano Re, Marcelo Naiouf and Armando Giusti
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Adriana Gaudiani: Universidad Nacional de General Sarmiento
Emilio Luque: Universidad Autónoma de Barcelona
Pablo García: Hydraulic Laboratory, National Institute of Water
Mariano Re: Hydraulic Laboratory, National Institute of Water
Marcelo Naiouf: Universidad Nacional de La Plata
Armando Giusti: Universidad Nacional de La Plata

Chapter Chapter 18 in Advances and New Trends in Environmental and Energy Informatics, 2016, pp 337-351 from Springer

Abstract: Abstract High performance computing has become a fundamental technology essential for computer simulation. Modelling and computational simulation provide powerful tools which enable flood event forecasting. In order to reduce flood damage, we have developed a methodology focused on enhancing a flood simulator minimizing the number of errors between simulated and observed results by using a two-phase optimization methodology via simulation. In this research, we implemented this approach to find the best solution or adjusted set of simulator input parameters. As a result of this, we achieved an improvement of up to 14 % which, for example, represents a significant difference of 0.5–1 m of water level along whole Paraná River basin. In order to find the adjusted set of input parameters, we reduced the search space using a Monte Carlo + clustering K-Means method. Therefore, an exhaustive search over the reduced search space led us to get a “good solution”. In summary, we propose add an improvement process on the classical computer model output to improve model quality.

Keywords: Flood simulation; Simulator tuning; Optimization methodology; Parametric simulation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-23455-7_18

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