Affiliations: [a] School of Energy Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi, China | [b] Key Lab for Western Safe Mining and Hazard Control, Ministry of Education, Xi'an University of Science and Technology, Xi'an, Shaanxi, China | [c] Xi'an Branch, Beijing Huayu Engineering Company Limited, China Coal Technology and Engineering Group, Xi'an, Shaanxi, China
Correspondence:
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Corresponding author: Wenyu Lv, School of Energy Engineering, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China. E-mail:lvwenyu@xust.edu.cn
Abstract: In order to effectively predict surface subsidence in backfilled coal mining
areas, a prediction model is developed based on an improved genetic
algorithm (GA) with optimized selection, crossover and mutation operators,
and an improved back-propagation neural network (BPNN). The GA-BPNN model
uses training and testing samples from mobile surface observation station
data in backfilled coal mining areas in China as well as carefully-selected
input variables. A comparison of calculated results from the GA-BPNN model
and conventional BPNN model with measured data shows that the GA-BPNN model
not only allows faster and more accurate learning, but also produces more
reliable prediction results. It also has higher fitting precision and
greater generalization abilities than conventional BPNN model, thus it is a
new theoretical approach to the surface subsidence calculation in backfilled
coal mining areas.
