基于IWOASVM的煤矿突水预测模型

针对传统煤矿突水预测算法易陷入局部最优、预测结果准确率低及速度慢等问题,提出了一种基于改进鲸鱼优化算法(IWOA)-支持向量机(SVM)的煤矿突水预测模型。IWOA从鲸鱼种群初始化、调节因子非线性化及随机差分进化(DE)3个方面入手对鲸鱼优化算法(WOA)进行改进:使用Tent映射初始化鲸鱼种群,提高鲸鱼种群寻找到最优猎物的可能性;通过调节因子非线性变化策略,提升算法在迭代前期的全局搜索能力及迭代后期的局部搜索能力,从而加快收敛速度;引入DE算法的变异、交叉、选择操作,以增强WOA的全局搜索能力。利用IWOA对SVM模型进行参数优化,将影响煤矿突水的水压、隔水层厚度、煤层倾角、断层落差、断层与工作层距离、采高共6个影响因素作为模型的输入特征向量,突水与安全2种突水结果作为模型的输出向量,以突水预测结果与实际结果间的误差最小化为目标建立目标函数,得到基于IWOA-SVM的煤矿突水预测模型。实验结果表明:与粒子群优化算法、DE算法、WOA相比,IWOA的预测准确率最高,标准误差最小,且收敛速度快,鲁棒性好;IWOA-SVM的突水预测准确率达到100%,与传统的突水系数法、SVM、WOA-SVM相比,IWOA-SVM表现出更高的准确率和稳定性。

Prediction model of water inrush in coal mine based on IWOA-SVM

The traditional prediction algorithm of water inrush in coal mine is easy to fall into local optimum,the prediction results accuracy is low and the speed is slow.In order to solve the above problems,a prediction model of water inrush in coal mine based on improved whale optimization algorithm(IWOA)and support vector machine(SVM)is proposed.IWOA improves the whale optimization algorithm(WOA)from three aspects,whale population initialization,nonlinear adjustment factor and random differential evolution(DE).Tent mapping is used to initialize the whale population to improve the possibility of the whale population finding the optimal prey.The non-linear change strategy of the adjustment factor is applied to improve the global search capability of the algorithm in the early stage of the iteration and the local search capability in the later stage of the iteration so as to speed up the convergence speed.The mutation,crossover and selection operations of DE algorithm are introduced to enhance the global search capability of WOA.The parameters of SVM model are optimized by IWOA.The six factors affecting water inrush in coal mine,including water pressure,thickness of aquiclude,dip angle of coal seam,fault drop,distance between fault and working face and mining height are taken as the input characteristic vectors of the model.The two water inrush results of water inrush and safety are taken as the output vectors.The objective function is established to minimize the error between the water inrush prediction results and the actual results,and the coal mine water inrush prediction model based on IWOA-SVM is obtained.The experimental results show that IWOA has the highest prediction accuracy,minimum standard error,fast convergence and good robustness compared with particle swarm optimization,DE algorithm and WOA.The accuracy of water inrush prediction of IWOA-SVM is 100%.Compared with the traditional water inrush coefficient method,SVM and WOA-SVM,IWOA-SVM shows higher accuracy and stability.