Prediction of Water-Richness Zoning of Weathered Bedrock Based on Whale Optimisation Algorithm and Random Forest

To effectively predict the water richness of weathered bedrock aquifers, the West First Plate area of the Hongliulin coal mine was taken as the study area, and 42 sets of pumping test borehole data from the weathered bedrock in the study area were used as training and testing samples. A total of five indicators related to the water richness of weathered bedrock, namely, the aquifer thickness, sand–base ratio, core take rate, degree of weathering, and lithological structure index, were selected. A prediction model for the water richness of weathered bedrock aquifers (WOA-RF) was subsequently proposed by combining the whale optimisation algorithm (WOA) and random forest (RF). This model can predict the water-richness level of weathered bedrock in an area with no pumping test data. The geological information from 98 sets of exploration boreholes in the study area was comprehensively used to achieve water-richness zoning of the weathered bedrock. The results indicated that the WOA is effective in optimising parameters and improving model performance. The accuracies of the optimal WOA-RF model in the training set and the test set were 93.1% and 92.3%, respectively. Compared with those of the single RF model, the accuracy, recall, and F1 value of the optimal WOA-RF model were increased by 11.3%, 18.2%, and 11%, respectively, and the differences before and after optimisation were obvious. A comparison and analysis of the predictive performance of each model revealed that the overall performance of the WOA-RF model was better than that of the other models. The weathered bedrock in the study area as a whole is weakly to moderately rich in water, and the predicted results are in good agreement with reality, which can provide a reference for future safe production in the West First Plate area.