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...

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Autores Principales: Veintimilla Reyes, Jaime Eduardo, Cisneros Espinoza, Felipe Eduardo, Vanegas Peralta, Pablo Fernando
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Publicado: ELSEVIER LTD 2018
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ANN
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
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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
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score 11,871979