Prediction of Mining-induced Ground Subsidence Using Support Vector Regression

机译：支持向量回归法预测矿山塌陷

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摘要

This study applies support vector regression techniques to develop empirical analysis model for estimation of mining-induced ground subsidence. The highly nonlinear relationship between the subsidence coefficient and affecting factors including qualitative and quantitative factors, such as mining parameters, rock mass mechanical properties, engineering geological conditions, and other relevant aspects was regressed from the field data. Typical cases collected from 30 coal mines located in different parts of China are used to train the support vector regression model and the trained model is then applied for prediction analysis to examine the efficiency of the current method. The application results show great performance of support vector algorithm which provides a promising alternative for prediction and prevention of mining-induced ground subsidence.

机译：这项研究应用支持向量回归技术来开发经验分析模型，以估计采矿引起的地面沉降。从现场数据中得出沉降系数与影响因素之间的高度非线性关系，这些影响因素包括定性和定量因素，例如开采参数，岩体力学特性，工程地质条件以及其他相关方面。从中国不同地区的30个煤矿收集的典型案例用于训练支持向量回归模型，然后将训练后的模型用于预测分析以检验当前方法的有效性。应用结果表明，支持向量算法具有良好的性能，为预测和预防开采引起的地面沉降提供了有希望的替代方法。

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来源
《2007采矿科学与安全技术国际学术会议》|2007年|242-247|共6页
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YANG Chengxiang; FENG Xiating;
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ground subsidence, support vector regression, nonlinear relationship, prediction, coal mining;

机译：地面沉降，支持向量回归，非线性关系，预测，煤矿开采;

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Prediction of Mining-induced Ground Subsidence Using Support Vector Regression

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