Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network

The El Nino Phenomenon is a climatic event whose consequences are devastating for Peru (Landslides, floods, etc). Due to this, in the present investigation a neural network is proposed, this RNA has the capacity to evaluate the level of vulnerability before this event of a building, more specificall...

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Autores principales:	Hugo David Calderon Vilca, Guillermo Moises Terrazas Garcia, Kevin Olivares Chuquiure, Carlos Ramirez Vera, Guido Raul Larico Uchamaco, Rene Alfredo Calderon Vilca
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	artificial neural nets vulnerability classifier el nino peru multilayer perceptron Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/bf4c6aa9d4584803bdfd969bf1900898
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spelling	oai:doaj.org-article:bf4c6aa9d4584803bdfd969bf19008982021-11-20T15:59:33ZLevel of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network2305-72542343-073710.5281/zenodo.5639759https://doaj.org/article/bf4c6aa9d4584803bdfd969bf19008982021-10-01T00:00:00Zhttps://www.fruct.org/publications/acm30/files/Cal2.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737The El Nino Phenomenon is a climatic event whose consequences are devastating for Peru (Landslides, floods, etc). Due to this, in the present investigation a neural network is proposed, this RNA has the capacity to evaluate the level of vulnerability before this event of a building, more specifically, an educational institution. An artificial multi-layer perceptron neural network was developed, trained with the backpropagation algorithm. This training was carried out using the results of the risk level assessment developed by the Ministry of Agriculture and Irrigation of Peru, approximately twelve thousand records were used. He developed two types of architectures with different number of neurons in the hidden layer. Finally, the first architecture was selected as the most suitable because it had a root error of 0.05, being less than the second architecture. With the training obtained in the first neuron, a web application was implemented to classify the level of vulnerability of an educational institution according to certain patterns present in it.Hugo David Calderon VilcaGuillermo Moises Terrazas GarciaKevin Olivares ChuquiureCarlos Ramirez VeraGuido Raul Larico UchamacoRene Alfredo Calderon VilcaFRUCTarticleartificial neural netsvulnerability classifierel ninoperumultilayer perceptronTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 2, Pp 324-330 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural nets vulnerability classifier el nino peru multilayer perceptron Telecommunication TK5101-6720
spellingShingle	artificial neural nets vulnerability classifier el nino peru multilayer perceptron Telecommunication TK5101-6720 Hugo David Calderon Vilca Guillermo Moises Terrazas Garcia Kevin Olivares Chuquiure Carlos Ramirez Vera Guido Raul Larico Uchamaco Rene Alfredo Calderon Vilca Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
description	The El Nino Phenomenon is a climatic event whose consequences are devastating for Peru (Landslides, floods, etc). Due to this, in the present investigation a neural network is proposed, this RNA has the capacity to evaluate the level of vulnerability before this event of a building, more specifically, an educational institution. An artificial multi-layer perceptron neural network was developed, trained with the backpropagation algorithm. This training was carried out using the results of the risk level assessment developed by the Ministry of Agriculture and Irrigation of Peru, approximately twelve thousand records were used. He developed two types of architectures with different number of neurons in the hidden layer. Finally, the first architecture was selected as the most suitable because it had a root error of 0.05, being less than the second architecture. With the training obtained in the first neuron, a web application was implemented to classify the level of vulnerability of an educational institution according to certain patterns present in it.
format	article
author	Hugo David Calderon Vilca Guillermo Moises Terrazas Garcia Kevin Olivares Chuquiure Carlos Ramirez Vera Guido Raul Larico Uchamaco Rene Alfredo Calderon Vilca
author_facet	Hugo David Calderon Vilca Guillermo Moises Terrazas Garcia Kevin Olivares Chuquiure Carlos Ramirez Vera Guido Raul Larico Uchamaco Rene Alfredo Calderon Vilca
author_sort	Hugo David Calderon Vilca
title	Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
title_short	Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
title_full	Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
title_fullStr	Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
title_full_unstemmed	Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network
title_sort	level of vulnerability of educational institutions in face el nino phenomenon and its classification with the neural network
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/bf4c6aa9d4584803bdfd969bf1900898
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Level of Vulnerability of Educational Institutions in Face El Nino Phenomenon and its Classification with the Neural Network

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