Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

Baddoo, Thelma Dede; Li, Zhijia; Odai, Samuel Nii; Boni, Kenneth Rodolphe Chabi; Nooni, Isaac Kwesi; Andam-Akorful, Samuel A.

doi:10.3390/ijerph18168375

Cited by 15 publications

(9 citation statements)

References 62 publications

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“…As part of the discussion to propose the methodological model, (Aguasca-Colomo et al 2019) y (Papailiou et al 2022), who present the treatment of data from meteorological stations in terms of the pre-processing of temporary data series, seeking to determine the behavior of the loss of data. For the methodology of eliminating the outlier data, (Kulanuwat et al 2021), (Azman et al 2021) and (Baddoo et al 2021), were reviewed with the aim of establishing a methodology that allows verification with statistical tests to determine the error and likewise establish the best method. And finally the methodology of (Chiu et al 2021) and (Addi et al 2022) for the analysis of results under statistical models such as root mean square error, mean absolute error, such as (Duarte et al 2022) that presents similar statistical indicators Finally, it was evaluated comparatively, therefore, the best behavior among the three methods was defined for its final implementation and thus completing the database for the next phase of the forecast project under a standard.…”

Section: A Methodologymentioning

confidence: 99%

Time Series Outlier Removal and Imputing Methods Based on Colombian Weather Stations Data

Parra-Plazas

Gaona-García

Plazas-Nossa

2022

Preprint

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The time data series of weather stations are a source of information for floods. The study of the previous wintertime series allows knowing the behavior of the variables and the result that will be applied to analysis and simulation models that feed variables such as flow and level of a study area. One of the most common problems is the acquisition and transmission of data from weather stations due to atypical values and lost data, this generates difficulties in the simulation process. Consequently, it is necessary to propose a numerical strategy to solve this problem. The data source for this study is a real database where these problems are presented with different variables of weather. This study is based on comparing three methods of time series analysis to evaluate a multivariable process offline. For the development of the study, we applied a method based on the Discrete Fourier Transform (DFT) and we contrasted it with methods such as the average and linear regression without uncertainty parameters to complete missing data. The proposed methodology entails statistical values, outlier detection and the application of the DFT. The application of DFT allows the time series completion, based on its ability to manage various gap sizes and replace missing values. In sum, DFT led to low error percentages for all the time series (1% average). This percentage reflects what would have likely been the shape or pattern of the time series behavior in the absence of misleading outliers and missing data.

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Section: A Methodologymentioning

confidence: 99%

Time Series Outlier Removal and Imputing Methods Based on Colombian Weather Stations Data

Parra-Plazas

Gaona-García

Plazas-Nossa

2022

Preprint

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“…The MD (missing data elimination techniques) rates using standard regression procedures and then combine the results of these analyses to obtain the final result, as shown in Eq. (1) [30,31]. MI using chained equations produces m imputations based on sequential imputation regression models of each variable conditioned by all other variables [30,32].…”

Section: Filling Of the Missed Data Using Multiple Imputations (Mi)mentioning

confidence: 99%

Trends in climatic and hydrological parameters in the Ajora-Woybo watershed, Omo-Gibe River basin, Ethiopia

2022

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The identification of hydro-meteorological trends is essential for analyzing climate change and river discharge at the watershed level. The Ajora-Woybo watershed in Ethiopia was studied for long-term trends in rainfall, temperature, and discharge at the annual, monthly, and seasonal time scales. The rainfall and temperature data extend 1990 to 2020, whereas the discharge data span from1990 to 2015. The Pettitt and Standard Normal Homogeneity Test (SNHT) tests were used to determine homogeneity. The Mann–Kendall and Sen's slope tests, as well as numerous variability measures, were then employed for trend analysis. The degree of relationship between climate variables and river discharge was determined using Pearson correlation coefficients (r). Inhomogeneity was discovered in annual rainfall data from the Angacha and Areka stations. Rainfall and discharge showed insignificant trends over time, with increasing and decreasing variability across stations. Monthly rainfall decreasing trends were observed to be significantly falling in February and March. Rainfall and runoff increase just insignificantly during the Kiremt season. On the other hand, minimum, maximum, and mean annual temperatures showed significant trends with annual increases of 0.01 °C, 0.04 °C, and 0.025 °C, respectively. In this study, the relationships between discharge and temperature and rainfall were found to be moderate and minimal, respectively. Generally, the results of the long-term examination of the hydrological and climate parameters in the watershed show that water resources vary throughout and over time. As a result, designing strategies require due attention. Article highlights Hydrological and meteorological parameters are essential for analyzing trends in water resource and climate change at the watershed level. Mann-Kendall and Sen's slope tests, along with a number of variability measures, were utilized in conjunction with the time series analysis approach for trend analysis. The analysis of the rainfall, temperature, and discharge in the watershed's data generally demonstrates how the availability of water resource varies over time. Designing suitable plans for water resource management and sustainable development in the watershed is therefore essential.

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“…In existing papers, the multivariate imputation method has been more frequently used than the univariate imputation method (which has rarely been used), when dealing with multivariate data [19][20][21]. However, since the time series characteristics of each variable can be extracted from multivariate data, this paper attempted to use univariate imputation and multivariate imputation simultaneously, in one piece of multivariate data.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

Choi

Jung

Cho

2021

Sensors

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With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables. However, in the existing method of imputing missing values, only one characteristic has been used and there has been no case where both characteristics were used. In this work, we introduced a new ensemble imputation method reflecting this. First, the cases in which missing values occur frequently were divided into four cases and were generated into the experimental data: communication error (aperiodic, periodic), sensor error (rapid change, measurement range). To compare the existing method with the proposed method, five methods of univariate imputation and five methods of multivariate imputation—both of which are widely used—were used as a single model to predict missing values for the four cases. The values predicted by a single model were applied to the ensemble method. Among the ensemble methods, the weighted average and stacking methods were used to derive the final predicted values and replace the missing values. Finally, the predicted values, substituted with the original data, were evaluated by a comparison between the mean absolute error (MAE) and the root mean square error (RMSE). The proposed ensemble method generally performed better than the single method. In addition, this method simultaneously considers the correlation between variables and time dependence, which are characteristics that must be considered in the environmental sensor. As a result, our proposed ensemble technique can contribute to the replacement of the missing values generated by environmental sensors, which can help to increase the reliability of environmental sensor data.

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Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

Cited by 15 publications

References 62 publications

Time Series Outlier Removal and Imputing Methods Based on Colombian Weather Stations Data

Time Series Outlier Removal and Imputing Methods Based on Colombian Weather Stations Data

Trends in climatic and hydrological parameters in the Ajora-Woybo watershed, Omo-Gibe River basin, Ethiopia

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics

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