Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events
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| Titel: | Evaluating different strategies for machine learning training applied to flow forecasting based on clustering of flood events |
|---|---|
| Autoren: | Patrícia Cristina Steffen, Júlio Gomes, Eloy Kaviski, Daniel Henrique Marco Detzel |
| Quelle: | Revista Brasileira de Recursos Hídricos, Vol 30 (2025) |
| Verlagsinformationen: | FapUNIFESP (SciELO), 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | TC401-506, Data-driven intelligent models, Technology, Hydraulic engineering, Hydrological modeling, Support Vector Regression algorithm, Artificial Intelligence techniques, River, lake, and water-supply engineering (General), Environmental sciences, Geography. Anthropology. Recreation, GE1-350, TC1-978, Fuzzy C-Means algorithm |
| Beschreibung: | 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. |
| Publikationsart: | Article |
| ISSN: | 2318-0331 1414-381X |
| DOI: | 10.1590/2318-0331.302520240087 |
| Zugangs-URL: | https://doaj.org/article/9e5024b51cff4a8a85095a4af0debb97 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....d07254b51ee9001c75c8f365ec2f9ce1 |
| Datenbank: | OpenAIRE |
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