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

Abstract

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.