Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis

• This research proposes an AI approach for slope evaluation.
• The method is based on the Least Squares Support Vector Classification.
• The Firefly Algorithm is used to optimize the assessment model.
• A dataset that contains 168 real slopes is utilized to construct the AI model.
• Experiments prove that the new method is a superior tool for slope evaluation.

Slope stability assessment is a critical research area in civil engineering. Disastrous consequences of slope collapse necessitate better tools for predicting their occurrences. This research proposes a hybrid Artificial Intelligence (AI) for slope stability assessment based on metaheuristic and machine learning. The contribution of this study to the body of knowledge is multifold. First, advantages of the Firefly Algorithm (FA) and the Least Squares Support Vector Classification (LS-SVC) are combined to establish an integrated slope prediction model. Second, an inner cross-validation with the operating characteristic curve computation is embedded in the training process to reliably construct the machine learning model. Third, the FA, an effective and easily implemented metaheuristic, is employed to optimize the model construction process by appropriately selecting the LS-SVM's hyper-parameters. Finally, a dataset that contains 168 real cases of slope evaluation, recorded in various countries, is used to establish and confirm the proposed hybrid approach. Experimental results demonstrate that the new hybrid AI model has achieved roughly 4% improvement in classification accuracy compared with other benchmark methods.