Abstract
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.
Highlights
Landslide is a common geological hazard that occurs worldwide every year
This study attempts to develop an integration model of grey relational analysis (GRA) and artificial neural network" (ANN) to predict the slope failure of circular slopes. 46 slope cases were collected from previous study is used to develop prediction model based on ANN
To improve the prediction performance, GRA is integrated with ANN during the pre-processing phase where GRA analyze all the input factors
Summary
Landslide is a common geological hazard that occurs worldwide every year. Slope failure is the most contributed factor to the landslide, triggered by excessive rainfall, earthquake, or human intervention such as deforestation and construction. Slope failures can be described as a movement of soil or rock downward the earth's gravity [1]. This natural disaster can cause significant damage to the environment and properties and contribute to the human loss of life. The prediction of slope failure remains a challenging task due to its complexity and uncertainties of geological factors and unbalance data samples. Traditional methods such as the limit equilibrium method (LEM), finite equilibrium method (FEM) are found to be computationally efficient, as reported by Ray [2]. With the introduction of the machine learning (ML) approach that is capable of modeling complex problems, the prediction of slope
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