A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield

The precise prediction of coal seam thickness in operating mines is crucial for the construction transparent mines. Geological borehole data or a small amount seismic information frequently used traditional methods; however, these methods have poor precision. In this study, we introduced model predicting based on comprehensive preference attribute combination (CPSAC) and least squares support vector machine (LS-SVM) optimized by whale optimization algorithm (WOA). We CPSAC to modify mass disturbed predict thickness. To achieve sample size was reduced optimizing combinations, modified entered into LS-SVM., Furthermore, create an accurate thickness, employed WOA determine optimal penalty coefficient kernel LS-SVM. An empirical case study conducted northeast mining area ZJ mine Huainan coalfield. two faces research were estimated compared, demonstrating proposed method’s high accuracy. method has guiding implications developing geological facilitating use resources.