Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions.

大坝变形监测和预测是评价水库安全性的关键。有几个因素会影响大坝变形。然而，这些元素的混合效应并不总是线性的。针对单核极限学习机泛化性能差、不稳定的问题，在本研究中，我们提出了一种基于混合核极限学习机的大坝变形预测改进蝙蝠算法。为了提高全局核的学习能力和局部核的泛化能力，我们结合了全局核函数（多项式核函数）和局部核函数（高斯核函数）。此外，为了克服蝙蝠算法的不足，提出了一种 Lévy 飞行蝙蝠优化算法（LBA）。结果表明，我们的模型优于其他模型。这证明了我们提出的算法和方法可用于不同项目和地区的大坝变形监测和预测。