Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá
Precipitation directly affects the water supply of river basins and its prediction becomes the main objective in different investigations. However, historical records often show missing data due to instrumental, technical or human drawbacks. This limitation must be solved to avoid errors in subseque...
| Main Authors: | , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia - UPTC
2020
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| Subjects: | |
| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/12209 |
| _version_ | 1801706690843770880 |
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| author | Bello, Angie Milena Cuta, Julián Andrés García, Ehidy Karime |
| author_facet | Bello, Angie Milena Cuta, Julián Andrés García, Ehidy Karime |
| author_sort | Bello, Angie Milena |
| collection | OJS |
| description | Precipitation directly affects the water supply of river basins and its prediction becomes the main objective in different investigations. However, historical records often show missing data due to instrumental, technical or human drawbacks. This limitation must be solved to avoid errors in subsequent Analysis. This proposal deal with a similar problem for a data set about precipitation collected in the central part of Boyacá along the years 1974-2013. The performance of the imputation mechanisms of loss MCAR, MAR and MNAR was evaluated. All of them were implemented each one under either a multiple imputation with a random approach based on an allocation by the K-Nearest Neighbors method with spatial focus and an imputation by the Kalman smoothing method time focused approach. We measured the convergence of the descriptive statistics of the imputed value and the original value, and additionally, we compared the graphical adjustments and their probability distributions. Amelia was suggested as a better performance of imputation technique jointly with a gamma distribution associated to the missing data. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-12209 |
| institution | Revista Ingeniería, Investigación y Desarrollo |
| language | spa |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia - UPTC |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-122092021-08-03T19:46:28Z Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá TÉCNICAS DE IMPUTACIÓN PARA DATOS DE PRECIPITACIÓN MÁXIMA MENSUAL EN LA ZONA CENTRAL DE BOYACÁ Bello, Angie Milena Cuta, Julián Andrés García, Ehidy Karime Multiple imputation precipitation R-software temporal series Boyacá Imputación múltiple Precipitación R series temporales Boyacá Precipitation directly affects the water supply of river basins and its prediction becomes the main objective in different investigations. However, historical records often show missing data due to instrumental, technical or human drawbacks. This limitation must be solved to avoid errors in subsequent Analysis. This proposal deal with a similar problem for a data set about precipitation collected in the central part of Boyacá along the years 1974-2013. The performance of the imputation mechanisms of loss MCAR, MAR and MNAR was evaluated. All of them were implemented each one under either a multiple imputation with a random approach based on an allocation by the K-Nearest Neighbors method with spatial focus and an imputation by the Kalman smoothing method time focused approach. We measured the convergence of the descriptive statistics of the imputed value and the original value, and additionally, we compared the graphical adjustments and their probability distributions. Amelia was suggested as a better performance of imputation technique jointly with a gamma distribution associated to the missing data. La precipitación se encuentra relacionada directamente con el suministro de agua de las cuencas fluviales, convirtiéndose su predicción en un objetivo de estudio en diferentes investigaciones. Sin embargo, los registros históricos a menudo muestran datos faltantes debido a fallas instrumentales, técnicos o humanos. Esta limitación impacta directamente los resultados de los análisis estadísticos que puedan ser realizados posteriormente. Esta investigación aborda este problema para un conjunto de datos con características similares, recopilados en la parte central del departamento de Boyacá - Colombia para el período 1974-2013. Se evaluó el desempeño de los mecanismos de imputación de pérdida MCAR, MAR o MNAR, cada uno de estos se implementó usando una imputación múltiple con un enfoque aleatorio, una asignación por el método de K-Nearest Neighbors con enfoque espacial y una imputación por el método de suavizado de Kalman con enfoque temporal. Se midió la convergencia de los estadísticos descriptivos del valor imputado y el valor original y se realizó la comparación de los ajustes gráficos y sus distribuciones de probabilidad, sugiriendo un mejor ajuste usando la imputación múltiple Amelia en conjunto con un ajuste a una distribución gamma para los datos faltantes en el conjunto de datos de referencia. Universidad Pedagógica y Tecnológica de Colombia - UPTC 2020-12-09 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/12209 10.19053/1900771X.v19.n1.2019.12209 Ingeniería Investigación y Desarrollo; Vol. 19 No. 1 (2019): Revista Ingeniería Investigación y Desarrollo; 64-79 Ingeniería Investigación y Desarrollo; Vol. 19 Núm. 1 (2019): Enero - Junio; 64-79 2422-4324 1900-771X spa https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/12209/9961 |
| spellingShingle | Multiple imputation precipitation R-software temporal series Boyacá Imputación múltiple Precipitación R series temporales Boyacá Bello, Angie Milena Cuta, Julián Andrés García, Ehidy Karime Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title | Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title_alt | TÉCNICAS DE IMPUTACIÓN PARA DATOS DE PRECIPITACIÓN MÁXIMA MENSUAL EN LA ZONA CENTRAL DE BOYACÁ |
| title_full | Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title_fullStr | Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title_full_unstemmed | Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title_short | Imputation techniques applied in a maximum monthly precipitation data in the central zone of Boyacá |
| title_sort | imputation techniques applied in a maximum monthly precipitation data in the central zone of boyaca |
| topic | Multiple imputation precipitation R-software temporal series Boyacá Imputación múltiple Precipitación R series temporales Boyacá |
| topic_facet | Multiple imputation precipitation R-software temporal series Boyacá Imputación múltiple Precipitación R series temporales Boyacá |
| url | https://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/12209 |
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