Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/29236
Title: Artificial neural networks applied to flow prediction: A use case for the Tomebamba river
Authors: Veintimilla Reyes, Jaime Eduardo
Cisneros Espinosa, Felipe Eduardo francisco
Vanegas Peralta, Pablo Fernando
Keywords: Artificial Neural Networks
Ann
Forecasting
Hydrology
Floods
Issue Date: 2016
Publisher: Elsevier Ltd
metadata.dc.description.city: 
Chania, Creta
metadata.dc.type: ARTÍCULO DE CONFERENCIA
Abstract: 
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.
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.
URI: https://www.sciencedirect.com/science/article/pii/S1877705816333367?via%3Dihub
ISBN: 000-000-000-0
ISSN: 1877-7058
Appears in Collections:Artículos

Files in This Item:
File Description SizeFormat 
documento.pdfdocument2.16 MBAdobe PDFView/Open


This item is protected by original copyright



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Centro de Documentacion Regional "Juan Bautista Vázquez"

Biblioteca Campus Central Biblioteca Campus Salud Biblioteca Campus Yanuncay
Av. 12 de Abril y Calle Agustín Cueva, Telf: 4051000 Ext. 1311, 1312, 1313, 1314. Horario de atención: Lunes-Viernes: 07H00-21H00. Sábados: 08H00-12H00 Av. El Paraíso 3-52, detrás del Hospital Regional "Vicente Corral Moscoso", Telf: 4051000 Ext. 3144. Horario de atención: Lunes-Viernes: 07H00-19H00 Av. 12 de Octubre y Diego de Tapia, antiguo Colegio Orientalista, Telf: 4051000 Ext. 3535 2810706 Ext. 116. Horario de atención: Lunes-Viernes: 07H30-19H00