Comparison of prediction models for the development height of water-conducting fractured zone

Abstract

In order to improve the accuracy of the prediction of the development height of the water-conducting fissure zone, by collecting the measured data of the lead height in the areas with similar geological and mining conditions in the past, the four factors of mining height, burial depth, inclination angle and working face slope length are comprehensively analyzed as the lead height of the algorithm model. The main influencing factors are to analyze and study the development characteristics of water-conducting fissures in the mining overburden by using a combination of engineering detection, machine learning and numerical simulation. Through drilling experiments and numerical control camera technology, the development height of the water-conducting fracture zone under the conditions of fully-mechanized mining in shallow coal seams was measured; by constructing an adaptive particle swarm optimization algorithm based on optimized least squares support vector machine regression algorithm (APSO-LSSVR) and UDEC The numerical simulation of the lead height prediction model, combined with the actual measured lead height data, determines the calculation method of the damage height of the overlying strata in Huaning Coal Mine. The results show that the measured development height of the water-conducting fracture zone in the study area is between 60.3~90.6 m; the goodness of fit between the predicted result of the lead height model based on the APSO-LSSVR algorithm and the true value is 94.79%, and the root mean square error is 1.6523 , The prediction accuracy is high, and it is applied to the prediction of the height of the water-conducting fissure zone of different working faces in the study area. Compared with the measured data, the average relative error of this model is 1.36%. Compared with the traditional UDEC numerical simulation prediction method, Its accuracy is relatively improved by 9.03%. It can be seen that the support vector machine model optimized by the adaptive particle swarm algorithm has higher processing performance for data collections with smaller data characteristics, can better reflect the development of water-conducting fissures, and can meet actual mining needs; it will be based on APSO -LSSVR's water-conducting fracture zone development height prediction model is applied to the 22109 and 22110 working faces to be mined in the study area, and it is concluded that the guided height of the 22109 working face is 62.7 m, and the guided height of the 22110 working face is 67.3 m.

Keywords

• water conducting fracture zone
• main control factor
• support vector machine
• numerical simulation
• height prediction