A Comparison of Artificial Neural Networks and Exponential Function Methods for Predicting Surface Subsidence in Underground Mining Areas

Abstract

In mining activities, surface subsidence is an extremely important issue. Therefore, predicting surface subsidence is a necessary task to ensure the safety and efficiency of mining operations. This study aims to evaluate the prediction results obtained from the Artificial Neural Networks (ANN) and Exponential Function (EF) methods for predicting surface subsidence in underground mining areas. To achieve this objective, data from four observation points on the Quang Hanh mine surface were utilized for comparison with the prediction results. For both methods, data from the first 14 monitoring cycles were used as the training dataset to develop the prediction models, while the last 4 cycles were reserved for evaluating their accuracy. The results show the absolute errors of the ANN are determined to be small, within the range of 10 mm; while it is of 30 mm for EF method. Additionally, the ANN model demonstrated an accuracy improvement of approximately 10% over the EF method. These findings highlight the potential of the ANN model as a reliable and accurate tool for predicting surface subsidence in mining areas.