Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest

Water level variation of explorational boreholes in mining sites is one of the most direct representations of water inrush risk. Despite recent efforts on mine water inrush accident prediction based on various types of observation data including water level of boreholes using a wide range of machine learning models, the accuracy and timeliness of the prediction for major accidents are still unsatisfactory. In this study, a novel prediction method based on the analysis of water level variation anomalies in borehole groups is proposed using the coupled model of long short-time memory (LSTM) and Isolation forest (IForest). Multi-variate LSTM algorithm is firstly used to model the correlated time sequence water level data in the borehole group, followed by the full extraction of the data variation feature based on the combination of variables including the prediction error being used as inputs of the IForest algorithm. As a result, anomalous variations of the data are detected as possible precursors of water inrush accident. Finally, a set of water inrush risk level grading rule is established based on the number of simultaneous anomalous data instances being detected within a borehole group. The results obtained for the water inrush accident in Xingdong coal mine demonstrate extraordinary timeliness and high accuracy as high water-inrush risk warning is issued for more than 2 days earlier than the time of actual sighting of the accident with very few false alarms being caused throughout the period of the testing dataset. This study provides an innovative solution to the low prediction accuracy and slow responding speed during major water inrush acci-dents of existing methods. The major parameters of the proposed coupled model of LSTM + IForest can also be easily calibrated based on traditional empirical methods for the best performance, allowing the method to be widely applied in various mining conditions.

Automated summary

In this study, a novel prediction method based on the analysis of water level variation anomalies in borehole groups is proposed using the coupled model of long short-time memory (LSTM) and Isolation forest (IForest). The method accurately and timely detects anomalous variations of water level data as possible precursors of water inrush accidents and establishes a set of risk level grading rule. The method demonstrated extraordinary timeliness and high accuracy in predicting water inrush accidents in Xingdong coal mine.