底板采动破坏深度统计学方法研究
Research on Statistical Methods of Mining Failure Depth of Floor

底板采动破坏深度的研究对煤矿突水危险性评价具有重要意义，随着科学计算的兴起，以统计学方法研究底板破坏深度越来越普遍。文章基于前人研究成果系统梳理了回归分析、支持向量机、灰色预测和BP神经网络四种算法的原理及其在底板破坏深度确定方面的应用，对底板破坏深度统计学方法研究的方向进行了展望，指出随着计算机技术的发展，底板破坏深度的确定会朝着更加多元化、精准化方向发展，基于机器学习的多方法融合分析是未来底板采动破坏深度研究的主要方法之一。
The research on mining failure depth of flooris of great significance to the risk assessment of water inrush in coal mines. With the rise of scientific calculation, it is more and more common to study the failure depth of floor by statistical methods. Based on the previous research results, this paper systematically combs the principle of regression analysis, support vector machine, grey prediction and BP neural network and their application in the determination of failure depth of floor, and looks forward to the research direction of statistical methods of failure depth of floor. It points out that with the development of computer technology, the determination of failure depth of floor will be more diversified and accurate. In the future, multi method fusion analysis based on machine learning is one of the main methods to study the mining failure depth of floor.