Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries.

To study the seepage law and sectional morphological characteristics in splitting sandstones of different grain sizes, three‐dimensional morphological scanning tests and seepage characteristics tests are conducted on the splitting rock samples of siltstone, fine‐, medium‐ and coarse‐grained sandstone, and the differences in morphological characteristics and seepage law in different grain size sandstones are compared and analyzed. The test results show that the sections of siltstone and fine‐grained sandstone have the overall morphological characteristics of “one high and one low” with large undulation, while the sections of medium‐ and coarse‐grained sandstone have a more uniform distribution and less undulation. In the relationship between pressure gradient and flow velocity, siltstone and fine‐grained sandstone are precisely characterized by the Forchheimer formula, and medium‐ and coarse‐grained sandstone are well characterized by the Izbash formula, and the fitting effect is good. The results of the study are of great reference significance for improving the cross‐sectional morphological characteristics and seepage law of the splitting rock samples.