改进蝙蝠算法优化极限学习机的大坝变形预测模型分析

Dam deformation prediction model based on IBA-ELM model

针对大坝变形监测数据中存在的非线性关系强和传统大坝预测模型精度不高等问题,本文利用改进蝙蝠算法选取最优的参数作为极限学习机的连接权值和阈值,并提出了一种基于改进蝙蝠算法(IBA)优化极限学习机(ELM)的大坝变形预测模型(IBA-ELM)。将IBA-ELM模型应用于工程实例,通过对某地水库大坝监测数据预测分析,验证IBA-ELM模型、BA-ELM和GA-ELM模型预测结果并进行精度评价,3种模型的预测值与实测值平均绝对误差分别为1.178 3、0.459 8、0.335 6 mm, IBA-ELM模型的预测精度高于另外2种模型,表明IBA-ELM模型能有效提高大坝变形预测能力。

Aiming at the problems of strong nonlinear relationship in dam deformation monitoring data and low precision of traditional dam prediction model, a dam deformation prediction model based on improved bat algorithm(IBA) and optimized extreme learning machine(ELM) is proposed by using the improved bat algorithm to select the optimal parameters as the connection weights and thresholds of the extreme learning machine(IBA-ELM). The IBA-ELM model is applied to an engineering case, and the accuracy of IBA-ELM, BA-ELM and GA-ELM model prediction results are evaluated through the prediction and analysis of dam monitoring data of a reservoir. The average absolute errors between predicted values and measured values are 1.178 3, 0.459 8 and 0.335 6, IBA-The prediction accuracy of ELM model is higher than the other two models, which indicates that IBA-ELM model can effectively improve the prediction ability of dam deformation and has certain application value.