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 failur...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Conference Paper |
Published: |
EDP Sciences
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-25901 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-259012023-05-29T17:05:29Z Integration of grey analysis with artificial neural network for classification of slope failure Deris A.M. Solemon B. Omar R.C. 54893136600 24832320000 35753735300 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. � The Authors, published by EDP Sciences. Final 2023-05-29T09:05:29Z 2023-05-29T09:05:29Z 2021 Conference Paper 10.1051/e3sconf/202132501008 2-s2.0-85146743287 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146743287&doi=10.1051%2fe3sconf%2f202132501008&partnerID=40&md5=caa248b3cd5ccdcd0841f8bf16fa0191 https://irepository.uniten.edu.my/handle/123456789/25901 325 1008 All Open Access, Gold EDP Sciences Scopus |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
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. � The Authors, published by EDP Sciences. |
author2 |
54893136600 |
author_facet |
54893136600 Deris A.M. Solemon B. Omar R.C. |
format |
Conference Paper |
author |
Deris A.M. Solemon B. Omar R.C. |
spellingShingle |
Deris A.M. Solemon B. Omar R.C. Integration of grey analysis with artificial neural network for classification of slope failure |
author_sort |
Deris A.M. |
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 |
2023 |
_version_ |
1806426486990700544 |
score |
13.18916 |