Open AccessSPATIAL MODELING VARIOUS TYPES OF SLOPE FAILURE USING ARTIFICIAL NEURAL NETWORK (ANN) IN PULAU PINANG, MALAYSIA/PEMODELAN RUANGAN PELBAGAI JENIS KEGAGALAN CERUN MENGGUNAKAN RANGKAIAN SARAF BUATAN (ANN) DI PULAU PINANG, MALAYSIA
Author(s) -
Nuriah Abd Majid,
Ruslan Rainis,
Wan Mohd Muhiyuddin Wan Ibrahim
Publication year - 2018
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.191
H-Index - 22
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v80.10483
Subject(s) - slope failure , artificial neural network , geotechnical engineering , elevation (ballistics) , three gorges , slope stability , geology , engineering , structural engineering , artificial intelligence , computer science
This paper discusses the modeling of various types of slope failure using the artificial neural network (ANN) in Penang. Slope failure areas identified by field trips. However, the existing models do not categorize the various types of slope failure, therefore the model to be developed will categorize a variety of types of slope failure has occurred The objective of the study is to model various types of slope failure using an artificial neural network (ANN). A total of 12 variables that influence the occurrence of slope failure is used to develop spatial model of slope failure. Among the factors are distant from slope failure to road, distant from slope failure to river, distant from slope failure to lineament, lithology, land use, soil series, average annual rainfall, slope aspect, slope steepness, topographic elevation (DEM), the curvature of the slope and vegetation index. The results of this study show a satisfactory performance in which the accuracy of the original model is 76.63%. The performance of the model is evaluated using independent data set of 20%, and the accuracy of 73.85%.