基于AdaBoost-PSO-ELM算法的滑坡位移预测研究

Landslide displacement prediction based on AdaBoost-PSO-ELM algorithm

矿山排土场滑坡的过程是一个动态、大延迟、高度非线性的特性问题,影响矿山排土场滑坡的因素众多,各个特性指标间相互影响,关于排土场滑坡预警并没有严格的划分标准。对此,提出一种自适应提升算法(Adaptive Boosting, AdaBoost)、改进的粒子群算法(Particle Swarm Optimization, PSO)和极限学习机(Extreme Learning Ma chine, ELM)相结合的矿山排土场滑坡短期预测方法。该方法首先利用粒子群优化算法得出ELM模型的最佳输入参数,再通过自适应提升算法将得到的多个极限学习机弱预测器组成新的强预测器并进行预测,最后以某矿山排土场采集的数据为算例,结果表明改进的组合方法的预测精度明显优于由粒子群优化算法优化参数的极限学习机模型和单独的极限学习机模型的预测精度,其预测结果接近于真实值,为实现矿山排土场滑坡预警提供了可能。

The process of landslides in mine dumps is a dynamic,large-delay,highly nonlinear characteristic problem.There are many factors affecting the landslide of mine dumps,and each characteristic index has mutual influence.But there is no strict division standard of landslide warning for dumping sites,this paper proposes a method of combining Adaptive Boosting(AdaBoost),improved Particle Swarm Optimization(PSO)and Extreme Learning Machine(ELM)for short-term prediction of mine dumps.Firstly,the particle swarm optimization algorithm is used to obtain the optimal input parameters of the ELM model.Then,the adaptive learning algorithm is used to form a plurality of extreme learning machine weak predictors into a new strong predictor and predict it.The data collected by the soil field is taken as an example.The results show that the improved particle swarm optimization algorithm,adaptive lifting algorithm and extreme learning machine model combination method have better prediction accuracy than the extreme learning machine model optimized by particle swarm optimization algorithm and separate one.The prediction accuracy of the extreme learning machine model is close to the true value,which provides a possibility to realize the landslide warning of mine dumps.