Modelagem de Séries Temporais Sazonais na Presença de Ooutliers Estudo de Caso da Vazão Máxima Mensal do Rio Jucu, ES, Brasil

Teixeira, Edmilson; Molinares, Fabio Fajardo; Reisen, Valdério Anselmo

doi:10.21168/rbrh.v13n2.p45-53

Cited by 2 publications

(4 citation statements)

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“…In the case under study, the smoothing parameter λ was estimated, as suggested by Box and Cox (1964) to define the type of transformation. According to Reisen et al (2008), often the transformation not only stabilizes the variance but also improves the data distribution approximation to the Normal distribution. Thus, all imputations were performed using the natural logarithm transformation in order to improve the approximation with the Rev.…”

Section: Results and Dicussionmentioning

confidence: 99%

Methodological approaches for imputing missing data into monthly flows series

Bleidorn¹,

Pinto²,

Schmidt³

et al. 2022

Rev. ambiente água

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Missing data is one of the main difficulties in working with fluviometric records. Database gaps may result from fluviometric stations components problems, monitoring interruptions and lack of observers. Incomplete series analysis generates uncertain results, negatively impacting water resources management. Thus, proper missing data consideration is very important to ensure better information quality. This work aims to analyze, comparatively, missing data imputation methodologies in monthly river-flow time series, considering, as a case study, the Doce River, located in Southeast Brazil. Missing data were simulated in 5%, 10%, 15%, 25% and 40% proportions following a random distribution pattern, ignoring the missing data generation mechanisms. Ten missing data imputation methodologies were used: arithmetic mean, median, simple and multiple linear regression, regional weighting, spline and Stineman interpolation, Kalman smoothing, multiple imputation and maximum likelihood. Their performances were compared through bias, root mean square error, absolute mean percentage error, determination coefficient and concordance index. Results indicate that for 5% missing data, any methodology for imputing can be considered, recommending caution for arithmetic mean method application. However, as the missing data proportion increases, it is recommended to use multiple imputation and maximum likelihood methodologies when there are support stations for imputation, and the Stineman interpolation and Kalman Smoothing methods when only the studied series is available. Keywords: Doce river, imputation, missing data.

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Section: Results and Dicussionmentioning

confidence: 99%

Methodological approaches for imputing missing data into monthly flows series

Bleidorn¹,

Pinto²,

Schmidt³

et al. 2022

Rev. ambiente água

View full text Add to dashboard Cite

show abstract

“…The Jucu River Watershed (Figure 1) is located in the Center-South region of Espírito Santo state, Brazil. The Jucu River is 166 km long and has a drainage area of 2.032 km² (Reisen et al, 2008). The Jucu River begins at the junction of the Jucu Braço Sul and Jucu Braço Norte Rivers and flows into Barra do Jucu Beach, in the municipality of Vila Velha.…”

Section: Study Areamentioning

confidence: 99%

“…Analyzing the performance of the series reconstruction, through visual analysis (Figure 11) and quality indicators (Table 2), the reconstruction procedure has an acceptable performance in processing the data homogeneity, allowing to eliminate the inconsistencies of flow series. It is even a viable alternative in the treatment of outliers because, according to Reisen et al (2008), its effects can significantly influence inferences made and, for example, generate errors in forecast analysis and decision-making in production processes, planning and management of water resources.…”

Section: Series Reconstructionmentioning

confidence: 99%

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Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

Bleidorn,

Schmidt,

Reis

et al. 2024

Rev. ambiente água

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Time series analysis is a useful tool for many practical water resource management applications, such as planning and anticipating conflict around water use. These analyses are also necessary for the use, design and operation of hydrotechnical works and for the protection of aquatic ecosystems. Basically, his review focuses on classic models such as Box and Jenkins. However, restrictive assumptions about data and residuals make its use difficult. As an alternative, non-parametric approaches are interesting due to their application versatility. One of the techniques that has been widely used in the analysis of flow time series is Singular Spectral Analysis (SSA). However, its application is concentrated as a pre-processor or in hybrid approaches and, because of this, many studies focus only on the model to be improved, leaving gaps in the process understanding that underlie the SSA. Within this context, and because of this, the objective of this study is to present the SSA technique in detail, through an application to a time series of monthly average flows with complex behavior (Jucu River, located in Southeast Brazil), in order to overcome gaps associated to a rigorous understanding of the procedures that make up the SSA. The Sequential SSA technique was used to decompose signal and noise components. The reconstructed series preserved the dynamics of the observed series, suggesting a strong influence of the signal component (trend and seasonality) on its behavior. It’s expected that the technique can be used with greater fluidity by specialists and contribute to the management of water resources. Keywords: decomposition, reconstruction, singular spectrum analysis.

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Modelagem de Séries Temporais Sazonais na Presença de Ooutliers Estudo de Caso da Vazão Máxima Mensal do Rio Jucu, ES, Brasil

Cited by 2 publications

References 0 publications

Methodological approaches for imputing missing data into monthly flows series

Methodological approaches for imputing missing data into monthly flows series

Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

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