An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Abstract

The Knothe time function is a classical method in predicting the ground mining subsidence. Nevertheless, it does not take the observation data into account in the prediction process. The Kalman filter method can solve this issue at large extent. Taking a coal mining work face of Xishan Coalfield as an example, this research compares the performance of the traditional Knothe time function and that of the improved Knothe time function by using the Kalman filter method. The comparison results show that through an improvement by using the Kalman filter method, the RMSE is improved from 133.4 mm to 78.3 mm; ME, from 91.9 mm to 3.1 mm; and the relative error, from 8.1% to 5.7%. Meanwhile, the improved model has good astringency. These verify that the improved model has higher accuracy and reliability. Hence, this research presents an effective method in predicting ground subsidence of mining area by improving the Knothe time function using the Kalman filter method.