A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

Abstract

Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety.

Transforming Landslide Prediction: A Novel Approach Combining Numerical Methods and Advanced Correlation Analysis in Slope Stability Investigation

“…Descriptive statistics are fundamental to understanding and interpreting data. They are also critical for summarizing and describing the main features of data collection quantitatively and objectively [24]. They form the basis for more complex statistical analyses and are essential for exploring data, testing hypotheses, and making decisions in research and applied settings.…”

“…Although there have been improvements in geotechnical engineering, accurate prediction of slope instability based on displacement is still a difficult task [22]. The main reason for this is the complex interaction of elements such as soil or rock composition, climatic conditions, and human activities, all of which can lead to slope instability [23,24]. To overcome these limitations, this paper presents a new method that combines sophisticated correlation analysis with numerical modeling to improve the accuracy of landslide prediction based on displacement.…”