基于Entropy-Kmeans++的尾矿库安全风险分级预警模型

Tailings Pond Safety Risk Classification Early Warning Model Based on Entropy-Kmeans++

针对尾矿库安全监管难度大、安全防范意识不足的问题,提出了一种基于Entropy-Kmeans++的尾矿库安全风险分级预警模型。首先,以某尾矿库为例,根据尾矿库现状确定威胁尾矿库安全的主要因素,建立适用于该尾矿库的评价指标体系;其次,运用熵权法(Entropymethod)获取各指标对应的权重信息,得到各个数据点的风险综合评分;接着,采用Kmeans++算法对风险评价结果进行聚类,得到风险评价结果的划分等级。最后运用该模型确定每个数据点对应的风险等级。结果表明:尾矿库安全风险分为低风险、较低风险、中风险、较高风险、高风险5类,该模型的评价结果与该尾矿库的实际安全情况一致,能够有效对该尾矿库的安全风险进行预警。

Aiming at the difficulty of safety supervision of tailings ponds and the lack of awareness of safety precautions,a classification and early warning model of tailings ponds safety risk based on Entropy-Kmeans++was proposed.First,taking a tailings pond as an example,the main factors that threaten the safety of the tailings pond are determined according to the current situation of the tailings pond,and an evaluation index system suitable for the tailings pond is established;secondly,the entropy method is used to obtain the weight information corresponding to each index is used to obtain the comprehensive risk score of each data point;then,the Kmeans++algorithm is used to cluster the risk evaluation results to obtain the classification level of the risk evaluation results.Finally,the model is used to determine the risk level corresponding to each data point.The results show that the safety risks of tailings ponds are divided into five categories:low risk,low risk,medium risk,high risk,and high risk.The evaluation results of the model are consistent with the actual safety situation of the tailings pond,which can effectively warn the safety risk of the tailings pond.