Navigation Institute, Jimei University, Xiamen, China.
PLoS One. 2021 May 10;16(5):e0250948. doi: 10.1371/journal.pone.0250948. eCollection 2021.
Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collision danger between meeting ships. The number of collision accidents per unit time in a certain water area can be considered to describe the regional collision risk However, historical ship collision accidents have contingencies, small sample sizes and weak regularities; hence, ship collision conflicts can be used as a substitute for ship collision accidents in characterizing the maritime traffic safety situation and have become an important part of methods that quantitatively study the traffic safety problem and its countermeasures. In this work, an EMD-QPSO-LSSVM approach, which is a hybrid of empirical mode decomposition (EMD) and quantum-behaved particle swarm optimization (QPSO) optimized least squares support vector machine (LSSVM) model, is proposed to forecast ship collision conflicts. First, original ship collision conflict time series are decomposed into a collection of intrinsic mode functions (IMFs) and a residue with EMD. Second, both the IMF components and residue are applied to establish the corresponding LSSVM models, where the key parameters of the LSSVM are optimized by QPSO algorithm. Then, each subseries is predicted with the corresponding LSSVM. Finally, the prediction values of the original ship collision conflict datasets are calculated by the sum of the forecasting values of each subseries. The prediction results of the proposed method is compared with GM, Lasso regression method, EMD-ENN, and the predicted results indicate that the proposed method is efficient and can be used for the ship collision conflict prediction.
船舶碰撞事故是海上交通安全的主要威胁。碰撞事故会造成人员伤亡和环境污染。碰撞风险是航海人员和监视操作人员判断会遇船舶碰撞危险的主要指标。一定水域内单位时间内的碰撞事故次数可以用来描述区域碰撞风险。但是，历史船舶碰撞事故具有偶然性、样本量小且规律弱的特点；因此，船舶碰撞冲突可以替代船舶碰撞事故来描述海上交通安全状况，已成为定量研究交通安全问题及其对策的方法的重要组成部分。在这项工作中，提出了一种 EMD-QPSO-LSSVM 方法，该方法是经验模态分解 (EMD) 和量子行为粒子群优化 (QPSO) 优化最小二乘支持向量机 (LSSVM) 模型的混合方法，用于预测船舶碰撞冲突。首先，原始船舶碰撞冲突时间序列通过 EMD 分解为一组固有模态函数 (IMF) 和残差。其次，将 IMF 分量和残差都应用于建立相应的 LSSVM 模型，其中 LSSVM 的关键参数通过 QPSO 算法进行优化。然后，每个子序列都通过相应的 LSSVM 进行预测。最后，通过各子序列的预测值之和计算原始船舶碰撞冲突数据集的预测值。将所提出方法的预测结果与 GM、Lasso 回归方法、EMD-ENN 进行比较，预测结果表明该方法是有效的，可以用于船舶碰撞冲突预测。
