A Computational Tool for Time-Series Prediction of Mining-Induced Subsidence Based on Time-Effect Function and Geodetic Monitoring Data

Underground mining-induced land subsidence may cause serious damage to engineering structures (e.g., buildings or roads) therefore, it is necessary to predict the subsidence with the highest possible accuracy. This paper proposes a new method for estimating preliminary values of the parameters to the modified Knothe time function, resulting in an improved capability of predicting land subsidence. A computational tool incorporating the proposed method has been developed to practically and numerically facilitate the time-series prediction of mining subsidence. A case study at the Mong Duong colliery at Quang Ninh province in Vietnam was considered and back-analyzed to validate the capability and accuracy of the tool. The accuracy of the subsidence prediction was evaluated using Root Mean Square Errors (\( RMSE \)), Mean Absolute Errors (\( MAE \)), and the Correlation coefficient (\( r \)). The result showed that the proposed method predicted reasonably well both the calibrating dataset (\( RMSE \) = 15 mm, \( MAE \) = 13 mm, \( r \) = 0.996) and the validating dataset (\( RMSE \) = 44 mm, \( MAE \) = 37 mm, \( r \) = 0.857). Based on the comparison results, it is concluded that the developed tool incorporating the proposed method is suitable for predicting underground mining-induced land subsidence.