改进TOPSIS与GA-BP耦合的采空区危险性辨析

针对目前采空区危险性辨析过程繁冗且准确性低的问题,提出了改进的TOPSIS与神经网络耦合的辨析方法.首先,为提高训练样本采空区危险性辨析客观准确性,将理想解方法(TOPSIS)进行改进,分别利用绝对理想点以及IFAHP避免了由于理想解及权重变化引起的逆序现象,并利用各辨析指标不同危险等级的区间临界值实现了TOPSIS对采空区危险性等级划分.将改进TOPSIS运用于某矿山100组采空区进行危险性辨析并验证结果.然后,为简化辨析过程,使改进TOPSIS与GA-BP神经网络有效结合,以经过TOPSIS辨析的100组样本采空区对GA-BP训练得到神经网络模型并对5组样本进行危险等级输出,结果与事实相符.研究结果不仅提高了采空区危险性辨析的客观性,并为简化辨析过程提供了新的思路,提高了工程应用性.

Goafs′ Risk Discrimination Based on Improved TOPSIS Coupled with GA-BP

According to the complex process and low accuracy of goaf area risk discrimination， the improved TOPSIS coupled with neural network was proposed. Firstly， the TOPSIS method was improved and used on goaf area in order to enhance the objection and accuracy of sample goafs’ risk discrimination. Absolute ideal point and improved fuzzy analytic hierarchy process (IFAHP) objective were respectively used to avoid the reverse phenomenon caused by the change of ideal point and weights. And risk grade division was realized through different instability degree interval threshold value of discrimination index. The improved TOPSIS method was used to risk discrimination of 100 groups goaf area samples in a certain mine and results were validated. Then， in order to simplify the discrimination process， the improved TOPSIS and GA-BP neural network were combined effectively. Calculating neural network model was trained by the 100 groups sample data which were discriminated by improved TOPSIS， the 5 groups were discriminated by the model， and the discrimination results agree with the facts. The study results not only enhance the objection of goaf area risk discrimination， but also provide a new thought for simplifying the discrimination process and expand the engineering application in field.