Abstract

The utilization of machine learning (ML) for rockburst assessment is hindered by an incomplete problem formalization in prior studies, which have focused more on predictive accuracy and less on factors such as explainability and uncertainty quantification. Despite achieving commendable accuracies, practitioners remain apprehensive about real-time implementation due to concerns surrounding transparency and validity, particularly when dealing with challenging or unfamiliar rock samples. This uncertainty may result in arbitrary predictions which are mostly incorrect. Establishing trust in ML applications, particularly in critical domains like rockburst assessment, requires models to provide transparent reasoning, communicate uncertainty, and exercise caution when making assertive predictions with unfamiliar data. This paper addresses these concerns by introducing a novel framework that combines interpretable ML techniques specifically Shapley additive explanation (SHAP), with adaptive conformal prediction (CP), built atop extreme gradient boosting to establish a transparent and reliable predictive framework. The validity of the proposed framework was rigorously assessed using conformal metrics, including marginal coverage, conditional coverage, and average prediction set size. Additionally, the SHAP technique was employed to elucidate explanations for predictions flagged as unreliable through CP. The results justify the framework’s high effectiveness in generating valid predictions for rockburst grades, concurrently providing corresponding confidence levels and insights into the underlying mechanisms that drive these predictions. The proposed framework facilitates the identification of unreliable predictions, ensuring that only reliable predictions inform decision-making. This not only enhances user confidence in the predictive model but also contributes to the overall safety of underground engineering projects.

中文翻译：

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透明有效的岩爆评估框架：统一可解释的机器学习和保形预测

摘要

先前研究中问题形式化的不完整阻碍了机器学习（ML）在岩爆评估中的应用，这些研究更多地关注预测准确性，而较少关注可解释性和不确定性量化等因素。尽管取得了值得称赞的准确性，但由于对透明度和有效性的担忧，从业者仍然对实时实施感到担忧，特别是在处理具有挑战性或不熟悉的岩石样本时。这种不确定性可能会导致任意预测，而这些预测大多是错误的。建立对机器学习应用程序的信任，特别是在岩爆评估等关键领域，需要模型提供透明的推理、传达不确定性，并在使用不熟悉的数据做出自信的预测时保持谨慎。本文通过引入一种新颖的框架来解决这些问题，该框架将可解释的 ML 技术（特别是 Shapley 附加解释 (SHAP)）与自适应保形预测 (CP) 相结合，构建在极端梯度提升之上，以建立透明且可靠的预测框架。使用保形指标严格评估所提出框架的有效性，包括边际覆盖率、条件覆盖率和平均预测集大小。此外，SHAP 技术还用于阐明通过 CP 标记为不可靠的预测的解释。结果证明该框架在生成岩爆等级的有效预测方面具有高效性，同时提供相应的置信度和对驱动这些预测的潜在机制的见解。所提出的框架有助于识别不可靠的预测，确保只有可靠的预测才能为决策提供信息。这不仅增强了用户对预测模型的信心，而且有助于地下工程项目的整体安全。