Integration of grey analysis with artificial neural network for classification of slope failure

With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based o...

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Autores principales:	Deris Ashanira Mat, Solemon Badariah, Omar Rohayu Che
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/407209596ede4152b3473adec9b6bcc3
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spelling	oai:doaj.org-article:407209596ede4152b3473adec9b6bcc32021-12-02T17:11:56ZIntegration of grey analysis with artificial neural network for classification of slope failure2267-124210.1051/e3sconf/202132501008https://doaj.org/article/407209596ede4152b3473adec9b6bcc32021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/101/e3sconf_icst2021_01008.pdfhttps://doaj.org/toc/2267-1242With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward back-propagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.Deris Ashanira MatSolemon BadariahOmar Rohayu CheEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 325, p 01008 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Deris Ashanira Mat Solemon Badariah Omar Rohayu Che Integration of grey analysis with artificial neural network for classification of slope failure
description	With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward back-propagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.
format	article
author	Deris Ashanira Mat Solemon Badariah Omar Rohayu Che
author_facet	Deris Ashanira Mat Solemon Badariah Omar Rohayu Che
author_sort	Deris Ashanira Mat
title	Integration of grey analysis with artificial neural network for classification of slope failure
title_short	Integration of grey analysis with artificial neural network for classification of slope failure
title_full	Integration of grey analysis with artificial neural network for classification of slope failure
title_fullStr	Integration of grey analysis with artificial neural network for classification of slope failure
title_full_unstemmed	Integration of grey analysis with artificial neural network for classification of slope failure
title_sort	integration of grey analysis with artificial neural network for classification of slope failure
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/407209596ede4152b3473adec9b6bcc3
work_keys_str_mv	AT derisashaniramat integrationofgreyanalysiswithartificialneuralnetworkforclassificationofslopefailure AT solemonbadariah integrationofgreyanalysiswithartificialneuralnetworkforclassificationofslopefailure AT omarrohayuche integrationofgreyanalysiswithartificialneuralnetworkforclassificationofslopefailure
_version_	1718381428836663296

Integration of grey analysis with artificial neural network for classification of slope failure

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