Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River
The main aim of this research is to create a model based on Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba river, at real time and in a specific day of a year. As inputs, this research is using information of rainfall and flow of the stations along of the river. This i...
Guardado en:
Autores Principales: | , , |
---|---|
Formato: | Artículos |
Publicado: |
ELSEVIER LTD
2018
|
Materias: | |
Acceso en línea: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004075667&doi=10.1016%2fj.proeng.2016.11.031&partnerID=40&md5=baf7956a9a7cccde11bc2cb57967c0d4 http://dspace.ucuenca.edu.ec/handle/123456789/29236 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:localhost:123456789-29236 |
---|---|
recordtype |
dspace |
spelling |
oai:localhost:123456789-292362018-02-17T11:26:51Z Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River Veintimilla Reyes, Jaime Eduardo Cisneros Espinoza, Felipe Eduardo Vanegas Peralta, Pablo Fernando ANN Artificial Neural Networks Floods Forecasting Hydrology The main aim of this research is to create a model based on Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba river, at real time and in a specific day of a year. As inputs, this research is using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. For this article, we have selected two scenarios. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance. This research includes two ANN models: Backpropagation and a hybrid model between back propagation and OWO-HWO (output weight optimization-hidden weight optimization) to select the initial weights of the connection. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error is minimal. These predictions are useful to avoid floods in the city of Cuenca in Ecuador. Chania 2018-01-11T16:47:48Z 2018-01-11T16:47:48Z 2016-06-01 info:eu-repo/semantics/Article 18777058 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004075667&doi=10.1016%2fj.proeng.2016.11.031&partnerID=40&md5=baf7956a9a7cccde11bc2cb57967c0d4 http://dspace.ucuenca.edu.ec/handle/123456789/29236 10.1016/j.proeng.2016.11.031 en_US instname:Universidad de Cuenca reponame:Repositorio Digital de la Universidad de Cuenca info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/3.0/ec/ ELSEVIER LTD Procedia Engineering info:eu-repo/date/embargoEnd/2022-01-01 0:00 |
institution |
UCUENCA |
collection |
Repositorio UCUENCA |
universidades |
UCUENCA |
language |
|
format |
Artículos |
topic |
ANN Artificial Neural Networks Floods Forecasting Hydrology |
spellingShingle |
ANN Artificial Neural Networks Floods Forecasting Hydrology Veintimilla Reyes, Jaime Eduardo Cisneros Espinoza, Felipe Eduardo Vanegas Peralta, Pablo Fernando Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
description |
The main aim of this research is to create a model based on Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba river, at real time and in a specific day of a year. As inputs, this research is using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. For this article, we have selected two scenarios. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance. This research includes two ANN models: Backpropagation and a hybrid model between back propagation and OWO-HWO (output weight optimization-hidden weight optimization) to select the initial weights of the connection. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error is minimal. These predictions are useful to avoid floods in the city of Cuenca in Ecuador. |
author |
Veintimilla Reyes, Jaime Eduardo Cisneros Espinoza, Felipe Eduardo Vanegas Peralta, Pablo Fernando |
author_facet |
Veintimilla Reyes, Jaime Eduardo Cisneros Espinoza, Felipe Eduardo Vanegas Peralta, Pablo Fernando |
author_sort |
Veintimilla Reyes, Jaime Eduardo |
title |
Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
title_short |
Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
title_full |
Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
title_fullStr |
Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
title_full_unstemmed |
Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River |
title_sort |
artificial neural networks applied to flow prediction: a use case for the tomebamba river |
publisher |
ELSEVIER LTD |
publishDate |
2018 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004075667&doi=10.1016%2fj.proeng.2016.11.031&partnerID=40&md5=baf7956a9a7cccde11bc2cb57967c0d4 http://dspace.ucuenca.edu.ec/handle/123456789/29236 |
_version_ |
1635523465819717632 |
score |
11,871979 |