Prediction of strata settlement in undersea metal mining based on deep forest

Abstract

Abstract Undersea mining encounters challenges due to the presence of seawater. An influx of seawater into stop in undersea can result in enormous disaster. Predicting strata settlement is a crucial measure to ensure the safety of undersea mining. This study proposed an intelligent model based on deep forest (DF) to evaluate the strata settlement during undersea mining. Initially, the strata displacement was monitored in the Xishan mining area of Sanshandao gold mine, China. Comprehensive datasets encompassing roof displacement and twelve influencing factors were compiled from 120 observations. Then, these datasets were statistically analyzed and used to train the DF model. The developed DF model achieved a training R 2 of 0.971 and a testing R 2 of 0.936. Compared with other machine learning models, the DF model has superior performance in the prediction of strata settlement. Moreover, a graphical user interface was designed to facilitate the application of the DF model. Finally, to validate model feasibility, displacement monitoring was conducted in the Xinli mining area of Sanshandao gold mine. Additional datasets were collected to validate the capability of the DF model. The results suggested that the DF model can be used to predict strata subsidence in undersea mining effectively.

Keywords

• Undersea mining
• Strata settlement
• Deep forest
• Machine learning