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Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon
Citation
Santillán Sánchez, David and Fraile Ardanuy, José Jesús and Toledo Municio, Miguel Ángel (2013). Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon. In: "2013 International Joint Conference on Neural Networks (IJCNN)", 04/08/2013 - 09/08/2013, Dallas, Texas, EE.UU. pp. 1-8. https://doi.org/10.1109/IJCNN.2013.6707110.
Description
| Title: | Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon |
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| Author/s: |
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| Item Type: | Presentation at Congress or Conference (Article) |
| Event Title: | 2013 International Joint Conference on Neural Networks (IJCNN) |
| Event Dates: | 04/08/2013 - 09/08/2013 |
| Event Location: | Dallas, Texas, EE.UU |
| Title of Book: | 2013 International Joint Conference on Neural Networks (IJCNN) |
| Date: | 2013 |
| Subjects: | |
| Faculty: | E.T.S.I. Telecomunicación (UPM) |
| Department: | Tecnologías Especiales Aplicadas a la Aeronáutica [hasta 2014] |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon.
More information
| Item ID: | 30159 |
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| DC Identifier: | http://oa.upm.es/30159/ |
| OAI Identifier: | oai:oa.upm.es:30159 |
| DOI: | 10.1109/IJCNN.2013.6707110 |
| Official URL: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6707110&tag=1 |
| Deposited by: | Memoria Investigacion |
| Deposited on: | 02 Aug 2014 11:39 |
| Last Modified: | 17 Oct 2018 11:44 |
