Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

Abstract

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input metrics on the final rockburst intensity level. The collected data underwent pre-processing using the isolation forest algorithm and synthetic minority oversampling technique. The random forest model was optimized through 5-fold cross-validation and the Optuna framework, resulting in the establishment of an Optuna-random forest (Op-RF) model that generates decision rules through its internal decision tree, utilizing the properties of the random forest model. The model was further interpreted using the Shapley additive explanations algorithm, both locally and globally. The results demonstrate that the proposed model achieved an area under curve score of 0.984. In comparison to eight other machine learning models, the proposed Op-RF model demonstrated superior accuracy, precision, recall, and F1 score. The model provides a transparent explanation of the prediction process, linking impact characteristics to the final output. Additionally, a cloud deployment method for the rockburst intensity prediction model is provided and its effectiveness is demonstrated through engineering verification. The proposed model offers a new approach to the application of machine learning in rockburst intensity prediction.

Keywords

Rockburst intensity / Isolation forest / Synthetic minority oversampling / Random forest / Interpretable model

Supplementary material

Supplementary material to this article can be found at https://github.com/jackieshan282/IOR-RBAPP.

CRediT authorship contribution statement

Yaxi Shen: Writing - original draft, Visualization, Software, Formal analysis, Conceptualization. Shunchuan Wu: Writing - review & editing, Funding acquisition, Conceptualization. Yongbing Wang: Investigation, Data curation. Jiaxin Wang: Resources, Methodology. Zhiquan Yang: Validation, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was financially supported by the National Natural Science Foundation of China (Grant No. 51934003), the Yunnan Major Scientific and Technological Projects (Grant No. 202202AG050014) and the Yunnan Innovation Team (Grant No. 202105AE160023).

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