Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks
The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-10-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340919308728 |
| _version_ | 1819067137898053632 |
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| author | Teresita Canchala-Nastar Yesid Carvajal-Escobar Wilfredo Alfonso-Morales Wilmar Loaiza Cerón Eduardo Caicedo |
| author_facet | Teresita Canchala-Nastar Yesid Carvajal-Escobar Wilfredo Alfonso-Morales Wilmar Loaiza Cerón Eduardo Caicedo |
| author_sort | Teresita Canchala-Nastar |
| collection | DOAJ |
| description | The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis. This paper is intended to present the filling of missing data of monthly rainfall of 45 gauge stations located in southwestern Colombia. The series analyzed covers 34 years of observations between 1983 and 2016, available from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). The estimation of missing data was done using Non-linear Principal Component Analysis (NLPCA); a non-linear generalization of the standard Principal Component Analysis Method via an Artificial Neural Networks (ANN) approach. The best result was obtained using a network with a [45−44−45] architecture. The estimated mean squared error in the imputation of missing data was approximately 9.8 mm. month−1, showing that the NLPCA approach constitutes a powerful methodology in the imputation of missing rainfall data. The estimated rainfall dataset helps reduce uncertainty for further studies related to homogeneity analyses, conglomerates, trends, multivariate statistics and meteorological forecasts in regions with information deficits such as southwestern Colombia. Keywords: Missing data, Monthly Rainfall Data, Artificial neural networks, NLPCA |
| first_indexed | 2024-12-21T16:13:29Z |
| format | Article |
| id | doaj.art-d4737e5debfc47129b4c0e7246257709 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-21T16:13:29Z |
| publishDate | 2019-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-d4737e5debfc47129b4c0e72462577092022-12-21T18:57:45ZengElsevierData in Brief2352-34092019-10-0126Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networksTeresita Canchala-Nastar0Yesid Carvajal-Escobar1Wilfredo Alfonso-Morales2Wilmar Loaiza Cerón3Eduardo Caicedo4Grupo de Investigación en Ingeniería de Recursos Hídricos y Suelos (IREHISA), Escuela de Recursos Naturales y del Ambiente (EIDENAR), Facultad de Ingeniería, Universidad del Valle, Calle 13 # 100-00, Cali, Colombia; Corresponding author.Grupo de Investigación en Ingeniería de Recursos Hídricos y Suelos (IREHISA), Escuela de Recursos Naturales y del Ambiente (EIDENAR), Facultad de Ingeniería, Universidad del Valle, Calle 13 # 100-00, Cali, ColombiaGrupo de Percepción y Sistemas Inteligentes (PSI), Escuela de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad del Valle, Calle 13 # 100-00, Cali, ColombiaDepartamento de Geografía, Facultad de Humanidades, Universidad del Valle, Calle 13 # 100-00, Cali, ColombiaGrupo de Percepción y Sistemas Inteligentes (PSI), Escuela de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad del Valle, Calle 13 # 100-00, Cali, ColombiaThe success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis. This paper is intended to present the filling of missing data of monthly rainfall of 45 gauge stations located in southwestern Colombia. The series analyzed covers 34 years of observations between 1983 and 2016, available from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). The estimation of missing data was done using Non-linear Principal Component Analysis (NLPCA); a non-linear generalization of the standard Principal Component Analysis Method via an Artificial Neural Networks (ANN) approach. The best result was obtained using a network with a [45−44−45] architecture. The estimated mean squared error in the imputation of missing data was approximately 9.8 mm. month−1, showing that the NLPCA approach constitutes a powerful methodology in the imputation of missing rainfall data. The estimated rainfall dataset helps reduce uncertainty for further studies related to homogeneity analyses, conglomerates, trends, multivariate statistics and meteorological forecasts in regions with information deficits such as southwestern Colombia. Keywords: Missing data, Monthly Rainfall Data, Artificial neural networks, NLPCAhttp://www.sciencedirect.com/science/article/pii/S2352340919308728 |
| spellingShingle | Teresita Canchala-Nastar Yesid Carvajal-Escobar Wilfredo Alfonso-Morales Wilmar Loaiza Cerón Eduardo Caicedo Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks Data in Brief |
| title | Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks |
| title_full | Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks |
| title_fullStr | Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks |
| title_full_unstemmed | Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks |
| title_short | Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks |
| title_sort | estimation of missing data of monthly rainfall in southwestern colombia using artificial neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2352340919308728 |
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