A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

• Published in: Water (Basel) Vol. 10; no. 11; p. 1618
• Main Authors: Ma, Dan, Duan, Hongyu, Cai, Xin, Li, Zhenhua, Li, Qiang, Zhang, Qi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2018
• Subjects: Algorithms; Analysis; Aquifers; Coal mining; Fatalities; Forecasts and trends; Fuzzy sets; GA-SVM; Genetic algorithms; Geological structures; Geology; Global optimization; hazard prediction; Hydraulics; Hydrogeology; Measurement methods; Methods; mine floor; Mines; Neural networks; Optimization; Parameters; Predictions; Support vector machines; System theory; Water damage; water inrush; Water pressure; China
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w10111618

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.