38 resultados para Flood forecasting.
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Pós-graduação em Engenharia Civil - FEIS
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AIM: In this study, we evaluated and compared community attributes from a tropical deforested stream, located in a pasture area, in a period before (PRED I) and three times after (POSD I, II, and III) a flash flood, in order to investigate the existence of temporal modifications in community structure that suggests return to conditions previous to the flash flood. METHODS: Biota samples included algae, macrophytes, macroinvertebrates, and fish assemblages. Changes in stream physical structure we also evaluated. Similarity of the aquatic biota between pre and post-disturbance periods was examined by exploratory ordination, known as Non-Metric Multidimensional Scaling associated with Cluster Analysis, using quantitative and presence/absence Bray-Curtis similarity coefficients. Presence and absence data were used for multivariate correlation analysis (Relate Analysis) in order to investigate taxonomic composition similarity of biota between pre and post-disturbance periods. RESULTS: Our results evidenced channel simplification and an expressive decrease in richness and abundance of all taxa right after the flood, followed by subsequent increases of these parameters in the next three samples, indicating trends towards stream community recovery. Bray-Curtis similarity coefficients evidenced a greater community structure disparity among the period right after the flood and the subsequent ones. Multivariate correlation analysis evidenced a greater correlation between macroinvertebrates and algae/macrophytes, demonstrating the narrow relation between their recolonization dynamics. CONCLUSIONS: Despite overall community structure tended to return to previous conditions, recolonization after the flood was much slower than that reported in literature. Finally, the remarkably high flood impact along with the slow recolonization could be a result of the historical presence of anthropic impacts in the region, such as siltation, riparian forest complete depletion, and habitat simplification, which magnified the effects of a natural disturbance.
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The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)