In machine learning (ML)-based slope stability prediction studies, feature importance results
often vary across different algorithms, leading to inconsistent interpretations. This
issue arises because the importance of features differs depending on the algorithm applied
within the same study. To address this challenge, this study proposes a novel methodology
for obtaining a final, unified ranking of features by combining the feature importance rankings
of various ML algorithms using a Multi-Criteria Decision-Making (MCDM) technique.
This approach ensures a consistent and reliable feature ranking derived from the
results of successful ML models. Furthermore, the study demonstrates how performance
indicators of ML algorithms can be translated into criterion weights within the MCDM
framework. Hyperparameter optimization was applied to the ML models, achieving accuracy
rates between 70% and 92.5%. Successful algorithms were analyzed using SHapley
Additive Explanations (SHAP) to evaluate feature importance, and the results were
integrated into the proposed SHAP-MCDM methodology. The MULTIMOORA method,
a well-established MCDM technique, was employed to combine the SHAP rankings. The
results confirmed that a final feature ranking could be derived by merging different SHAP
rankings of ML algorithms using the proposed SHAP-MULTIMOORA approach. The
study also identified key features like cohesion, internal friction angle, and slope height,
which significantly influence slope stability prediction. This methodology both contributes
to the integration of SHAP rankings and advances prediction accuracy by calculating
criterion weights of ML algorithms based on multiple performance metrics. The proposed
approach has a broad applicability, improving both classification and regression-based
prediction tasks in various domains beyond slope stability.