In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the risk of railway dangerous goods transportation system (RDGTS). The statistical occurrence frequency of each sub-risk indicator of the happened RDGTS accidents is used as the basis of experts’ scores, the experts’ scores are presented in interval numbers form, which are used as the inputs of the four approaches. The accuracy rate, optimization time consuming, Mean Square Error (MSE), Receiver Operating Characteristic Curve (ROC) and Area Under Curve (AUC) are used as the evaluation indexes of the identification results. The comparison studies are conducted by using SVM with linear kernel (SVM-L) and SVM with polynomial kernel (SVM-P), respectively. By using such new methodology, the risk identification problem (evaluation problem) is transferred into a classification problem with faster identification speed, higher identification efficiency and higher accuracy. The identification results show that: GS-SVM is the optimal approach to identify the risk factors of Human; SVM is the optimal approach to identify the risk factors of Machine, Materials, Environment and Management. SVM has the shortest optimization time consuming, GA-SVM has the highest accuracy, hence, SVM and GA-SVM are better to applied to identify the risk of RDGTS. The optimization time consuming of all models is no more than 5 s, which means the RDGTS risk identification results could be obtained fast and high-efficiently and the mental strength for researchers can be reduced by using the SVM and improved SVM models. For the risk identification results considering MSE and AUC, GS-SVM is the most accurate and best classification algorithm, and the results based on SVM-P is better than the results based on SVM-L. The results based on PSO-SVM, GA-SVM and GS-SVM have better accuracy and reliability than that based on SVM-L or SVM-P, which means the PSO-SVM, GA-SVM and GS-SVM based risk identification approaches are practicable.

本文采用三种算法来获取支持向量机（SVM）的径向基函数（RBF）核的参数，包括：PSO（粒子群优化）、GA（遗传算法）和GS（网格搜索）。三种改进的支持向量机方法被应用于识别铁路危险品运输系统（RDGTS）的风险。以所发生的RDGTS事故的各个子风险指标的统计发生频率作为专家评分的依据，专家评分以区间数的形式呈现，作为四种方法的输入。以准确率、优化耗时、均方误差(MSE)、接收器操作特征曲线(ROC)和曲线下面积(AUC)作为识别结果的评价指标。分别使用线性核支持向量机 (SVM-L) 和多项式核支持向量机 (SVM-P) 进行比较研究。通过使用这种新方法，将风险识别问题（评估问题）转化为识别速度更快、识别效率更高、准确率更高的分类问题。识别结果表明：GS-SVM是识别Human危险因素的最优方法；SVM 是识别机器、材料、环境和管理风险因素的最佳方法。SVM 优化耗时最短，GA-SVM 精度最高，因此 SVM 和 GA-SVM 更适合用于识别 RDGTS 的风险。所有模型的优化耗时不超过5s，这意味着使用支持向量机和改进的支持向量机模型可以快速、高效地获得RDGTS风险识别结果，降低研究人员的精神力量。对于考虑MSE和AUC的风险识别结果，GS-SVM是最准确、最好的分类算法，基于SVM-P的结果优于基于SVM-L的结果。基于 PSO-SVM、GA-SVM 和 GS-SVM 的结果比基于 SVM-L 或 SVM-P 的结果具有更好的准确性和可靠性，这意味着基于 PSO-SVM、GA-SVM 和 GS-SVM 的风险识别方法是可行的。基于 SVM-P 的结果优于基于 SVM-L 的结果。基于 PSO-SVM、GA-SVM 和 GS-SVM 的结果比基于 SVM-L 或 SVM-P 的结果具有更好的准确性和可靠性，这意味着基于 PSO-SVM、GA-SVM 和 GS-SVM 的风险识别方法是可行的。基于 SVM-P 的结果优于基于 SVM-L 的结果。基于 PSO-SVM、GA-SVM 和 GS-SVM 的结果比基于 SVM-L 或 SVM-P 的结果具有更好的准确性和可靠性，这意味着基于 PSO-SVM、GA-SVM 和 GS-SVM 的风险识别方法是可行的。