A causal inference method for canal safety anomaly detection based on structural causal model and GBDT

Abstract

ABSTRACTIt is a common idea to take advantage of as much evidence as possible in engineering safety monitoring data analysis. Compared to the direct use of as many measurement points as input features in prediction tasks, this paper reveals that this habit tends to introduce a risk in structural anomaly detection. This paper proposes a machine learning based causal inference method, SCMGBDT structural causal model of gradient boosting decision tree (SCMGBDT), within a structural causal model framework to improve the robustness of the model in structural safety anomaly detection tasks in terms of three aspects. First, the causal effect generation relationship between environmental measurement points and correlated response measurement points is explained by constructing a common confounder causal graph; then, the GBDT machine learning method is introduced to discover the nonlinear statistical relationship between environmental measurement points and response measurement points; finally, the model parameter estimation results are improved by introducing regularisation constraints and cross-checking methods. By comparing the model estimation precision and the anomaly detection accuracy under different simulated anomaly scenarios, the results show that the SCMGBDT model proposed in this paper has a reasonable construction process and the model can maintain a good anomaly detection performance under different anomaly scenarios.

Keywords

• Hydraulic structure monitoring
• canal safety anomaly detection
• causal inference
• structural causal model
• gradient boosting decision tree
• Surveillance de la structure hydraulique ; détection d’anomalies de sécurité des canaux ; inférence causale ; modèle causal structurel ; arbre de décision de renforcement de gradient