Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador
Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by m...
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| Format: | Article |
| Language: | English |
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Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi)
2021-06-01
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| Series: | Revista Ambiente & Água |
| Subjects: | |
| Online Access: | https://www.scielo.br/j/ambiagua/a/m3nQgWQLmhHqPghwMKHtnNP/?lang=en |
| _version_ | 1818881304852168704 |
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| author | Diego Heras Carlos Matovelle |
| author_facet | Diego Heras Carlos Matovelle |
| author_sort | Diego Heras |
| collection | DOAJ |
| description | Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Cañar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed. |
| first_indexed | 2024-12-19T14:59:45Z |
| format | Article |
| id | doaj.art-e5623b8ac7e34b8f90bd2525165de699 |
| institution | Directory Open Access Journal |
| issn | 1980-993X |
| language | English |
| last_indexed | 2024-12-19T14:59:45Z |
| publishDate | 2021-06-01 |
| publisher | Instituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi) |
| record_format | Article |
| series | Revista Ambiente & Água |
| spelling | doaj.art-e5623b8ac7e34b8f90bd2525165de6992022-12-21T20:16:37ZengInstituto de Pesquisas Ambientais em Bacias Hidrográficas (IPABHi)Revista Ambiente & Água1980-993X2021-06-0116311210.4136/ambi-agua.2708Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - EcuadorDiego Heras0https://orcid.org/0000-0002-8729-0981Carlos Matovelle1https://orcid.org/0000-0003-2267-0323Center for Research, Innovation and technology transfer. Environmental Engineering. Catholic University of Cuenca, Avenida de las Americas, EC 010101, Cuenca, Azuay, Ecuador. Center for Research, Innovation and technology transfer. Environmental Engineering. Catholic University of Cuenca, Avenida de las Americas, EC 010101, Cuenca, Azuay, Ecuador. Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Cañar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed.https://www.scielo.br/j/ambiagua/a/m3nQgWQLmhHqPghwMKHtnNP/?lang=endata imputationhydrographic systemsmachine learning |
| spellingShingle | Diego Heras Carlos Matovelle Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador Revista Ambiente & Água data imputation hydrographic systems machine learning |
| title | Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador |
| title_full | Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador |
| title_fullStr | Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador |
| title_full_unstemmed | Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador |
| title_short | Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador |
| title_sort | machine learning methods for hydrological imputation data analysis of the goodness of fit of the model in hydrographic systems of the pacific ecuador |
| topic | data imputation hydrographic systems machine learning |
| url | https://www.scielo.br/j/ambiagua/a/m3nQgWQLmhHqPghwMKHtnNP/?lang=en |
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