Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events
ABSTRACT This paper presents a new hydrological modeling approach for discharge prediction based on flood clustering. Combined with Machine Learning techniques, river flow simulation is optimized through increased data similarity within clusters. Using daily mean discharge from 1964 to 2015 in União...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Associação Brasileira de Recursos Hídricos
2025-05-01
|
| Series: | Revista Brasileira de Recursos Hídricos |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100218&lng=en&tlng=en |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850142425002016768 |
|---|---|
| author | Patrícia Cristina Steffen Júlio Gomes Eloy Kaviski Daniel Henrique Marco Detzel |
| author_facet | Patrícia Cristina Steffen Júlio Gomes Eloy Kaviski Daniel Henrique Marco Detzel |
| author_sort | Patrícia Cristina Steffen |
| collection | DOAJ |
| description | ABSTRACT This paper presents a new hydrological modeling approach for discharge prediction based on flood clustering. Combined with Machine Learning techniques, river flow simulation is optimized through increased data similarity within clusters. Using daily mean discharge from 1964 to 2015 in União da Vitória (Iguaçu River basin, Paraná State, Brazil), the Fuzzy C-Means algorithm clustered flood events into three groups. So, five models were trained: one for the complete series, one for all flood events, and one for each cluster. The Support Vector Regression algorithm was used to develop Artificial Intelligence (AI) models, that had better performance in predicting discharge for each group they were trained and showed similar efficiency to the model trained for the entire series for a 1-day forecast time. The present paper discusses only the results from the training and testing phases. A future paper (in elaboration) will present the development and evaluation of the flow forecast models based on the proposed methodology. |
| format | Article |
| id | doaj-art-9e5024b51cff4a8a85095a4af0debb97 |
| institution | OA Journals |
| issn | 2318-0331 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Associação Brasileira de Recursos Hídricos |
| record_format | Article |
| series | Revista Brasileira de Recursos Hídricos |
| spelling | doaj-art-9e5024b51cff4a8a85095a4af0debb972025-08-20T02:29:04ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-03312025-05-013010.1590/2318-0331.302520240087Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood eventsPatrícia Cristina Steffenhttps://orcid.org/0000-0002-2672-4619Júlio Gomeshttps://orcid.org/0000-0003-0746-9875Eloy Kaviskihttps://orcid.org/0000-0002-3618-7976Daniel Henrique Marco Detzelhttps://orcid.org/0000-0003-2841-6502ABSTRACT This paper presents a new hydrological modeling approach for discharge prediction based on flood clustering. Combined with Machine Learning techniques, river flow simulation is optimized through increased data similarity within clusters. Using daily mean discharge from 1964 to 2015 in União da Vitória (Iguaçu River basin, Paraná State, Brazil), the Fuzzy C-Means algorithm clustered flood events into three groups. So, five models were trained: one for the complete series, one for all flood events, and one for each cluster. The Support Vector Regression algorithm was used to develop Artificial Intelligence (AI) models, that had better performance in predicting discharge for each group they were trained and showed similar efficiency to the model trained for the entire series for a 1-day forecast time. The present paper discusses only the results from the training and testing phases. A future paper (in elaboration) will present the development and evaluation of the flow forecast models based on the proposed methodology.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100218&lng=en&tlng=enArtificial Intelligence techniquesData-driven intelligent modelsFuzzy C-Means algorithmHydrological modelingSupport Vector Regression algorithm |
| spellingShingle | Patrícia Cristina Steffen Júlio Gomes Eloy Kaviski Daniel Henrique Marco Detzel Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events Revista Brasileira de Recursos Hídricos Artificial Intelligence techniques Data-driven intelligent models Fuzzy C-Means algorithm Hydrological modeling Support Vector Regression algorithm |
| title | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| title_full | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| title_fullStr | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| title_full_unstemmed | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| title_short | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| title_sort | evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
| topic | Artificial Intelligence techniques Data-driven intelligent models Fuzzy C-Means algorithm Hydrological modeling Support Vector Regression algorithm |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312025000100218&lng=en&tlng=en |
| work_keys_str_mv | AT patriciacristinasteffen evaluatingdifferentstrategiesformachinelearningtrainingappliedtoflowforecastingbasedonclusteringoffloodevents AT juliogomes evaluatingdifferentstrategiesformachinelearningtrainingappliedtoflowforecastingbasedonclusteringoffloodevents AT eloykaviski evaluatingdifferentstrategiesformachinelearningtrainingappliedtoflowforecastingbasedonclusteringoffloodevents AT danielhenriquemarcodetzel evaluatingdifferentstrategiesformachinelearningtrainingappliedtoflowforecastingbasedonclusteringoffloodevents |