Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin
The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make a...
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Escuela Superior Politécnica del Litoral
2023-10-01
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| Series: | Revista Tecnológica |
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| Online Access: | https://rte.espol.edu.ec/index.php/tecnologica/article/view/1028 |
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| author | Maria Daniela Gonzalez Dario Xavier Zhina Alexandra Guanuchi-Quito Alex Aviles-Anazco |
| author_facet | Maria Daniela Gonzalez Dario Xavier Zhina Alexandra Guanuchi-Quito Alex Aviles-Anazco |
| author_sort | Maria Daniela Gonzalez |
| collection | DOAJ |
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The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make appropriate decisions in extreme environments. This study seeks to predict discharges in the Paute river basin through global teleconnection indices. Multiple Linear Regression (MLR) was obtained using three different methodologies: multicollinearity analysis, Principal Component Analysis (PCA), and correlation with monthly delays. It was shown that the principal component analysis scenario obtained the best predictive fits, specifically by including 41 indices and 20 components. For the scenario using monthly delays, the best delay occurs within a single month for most seasons. Finally, with the multicollinearity analysis scenario, better results were obtained using 41 indices, although essentially the performance corresponds to the number and indices of each model. Teleconnection indices are not sufficient when used as the only input variable for download modeling and prediction, giving mostly unsatisfactory results. However, a clear trend links the behavior of flows and indices, and it is possible to improve the models based on more climatic variables or with other predictive methods.
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| format | Article |
| id | doaj-art-5b1e9b715f4b4f1bb4182432e87a3ec9 |
| institution | DOAJ |
| issn | 0257-1749 1390-3659 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Escuela Superior Politécnica del Litoral |
| record_format | Article |
| series | Revista Tecnológica |
| spelling | doaj-art-5b1e9b715f4b4f1bb4182432e87a3ec92025-08-20T03:13:44ZengEscuela Superior Politécnica del LitoralRevista Tecnológica0257-17491390-36592023-10-0135210.37815/rte.v35n2.1028Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basinMaria Daniela Gonzalez0https://orcid.org/0009-0003-3436-0805Dario Xavier Zhina1https://orcid.org/0000-0001-9556-4025Alexandra Guanuchi-Quito2https://orcid.org/0000-0002-5583-8674Alex Aviles-Anazco3https://orcid.org/0000-0001-9278-5738Facultad de Ciencias Químicas, Universidad de CuencaGrupo de Evaluación de riesgos ambientales en sistemas de produccion y servicios (RISKEN)Grupo de Evaluación de riesgos ambientales en sistemas de produccion y servicios (RISKEN)Grupo de Evaluación de riesgos ambientales en sistemas de produccion y servicios (RISKEN) The Paute river basin (southern Ecuador) suffers hydrological changes due to climate change and human activities. Hydrological changes cause extreme events and affect ecosystems, hydroelectric plants, and quality of life. It highlights the importance of understanding hydrological behavior to make appropriate decisions in extreme environments. This study seeks to predict discharges in the Paute river basin through global teleconnection indices. Multiple Linear Regression (MLR) was obtained using three different methodologies: multicollinearity analysis, Principal Component Analysis (PCA), and correlation with monthly delays. It was shown that the principal component analysis scenario obtained the best predictive fits, specifically by including 41 indices and 20 components. For the scenario using monthly delays, the best delay occurs within a single month for most seasons. Finally, with the multicollinearity analysis scenario, better results were obtained using 41 indices, although essentially the performance corresponds to the number and indices of each model. Teleconnection indices are not sufficient when used as the only input variable for download modeling and prediction, giving mostly unsatisfactory results. However, a clear trend links the behavior of flows and indices, and it is possible to improve the models based on more climatic variables or with other predictive methods. https://rte.espol.edu.ec/index.php/tecnologica/article/view/1028Discharge predictionTeleconnection indicesPrincipal component analysisMultiple regression modelsMulticollinearity analysis |
| spellingShingle | Maria Daniela Gonzalez Dario Xavier Zhina Alexandra Guanuchi-Quito Alex Aviles-Anazco Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin Revista Tecnológica Discharge prediction Teleconnection indices Principal component analysis Multiple regression models Multicollinearity analysis |
| title | Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin |
| title_full | Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin |
| title_fullStr | Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin |
| title_full_unstemmed | Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin |
| title_short | Comparison of methodologies for flow prediction through teleconnection indices. Case study: Paute river basin |
| title_sort | comparison of methodologies for flow prediction through teleconnection indices case study paute river basin |
| topic | Discharge prediction Teleconnection indices Principal component analysis Multiple regression models Multicollinearity analysis |
| url | https://rte.espol.edu.ec/index.php/tecnologica/article/view/1028 |
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