###### Abstract
[20] explored an improved Pauta criterion for a gross errors elimination algorithm based on M estimation, which effectively solved the problem of misjudgment of outliers; [PERSON] et al. [21] combined the advantages of the ability of robust resistance by robust estimation and the ability of handling nonlinear problems by extreme learning machine to establish the dam safety monitoring model of M-ELM. Compared with traditional methods, these methods are able to solve the problem of outliers better in dam deformation data, but there is still misjudgment when the monitoring sequence has continuous deviation from a normal value or low deviation degree. This phenomenon should be further studied to improve the identification ability of abnormal data. The current traditional dam safety monitoring model includes a mathematical statistical model, deterministic model and mixed model. As early as in the 1950s, Portugal, Italy and other countries began to utilize the statistical regression method to establish the dam deformation monitoring model [22; 23]. In the 1970s, [PERSON] et al. introduced statistical regression to establish the dam safety monitoring model, and explained the physical causes of the analysis results. In the mid-1980s, [PERSON] et al. [24; 25; 26] established the statistical model between measured data and effect size by utilizing mathematical statistical methods such as gradual regression and weighted regression, successfully applying it in a practical project. In recent years, with the development of computers, big data and artificial intelligence, intelligent algorithms such as gray system, fuzzy mathematics, Bayes classification algorithm, chaos, wavelet theory, random forest algorithm, cluster algorithm, neural network and particle swarm optimization [27] have been introduced into the field of dam safety monitoring. [PERSON] et al. [28] introduced nuclear partial least squares method into the safety monitoring of an ultra-high dam for the first time; [PERSON] et al. [29] constructed a dam deformation monitoring model based on the radial basis function (RBF) neural network; [PERSON] et al. [30] developed a monitoring factor excavation model for concrete dams based on the optimized random forest method; [PERSON] et al. [31] proposed three multi-target support vector regression models, and accordingly constructed a dam deformation behavior characterization model. [PERSON] et al. [32] utilized stacking and blending integration strategies to predict radial movements of an arch-gravity dam. The IGG method of selection iteration is the most commonly utilized method in robust estimation [33], and the selection of the initial value generally directly obtained through the least squares (LS). The LS-IGG method is able to accurately identify gross errors when the number of gross errors is relatively small. However, as the proportion of gross errors increases, the identification ability of this method reduces significantly, and gross errors cannot be accurately eliminated. Therefore, in this paper gross errors identification and regression analysis model is established by combining the IGG method and extreme learning machine (ELM) for gross errors identification and regression analysis of the dam detection data with the Least Median Squares (LMS) method for selecting the initial value of robust resistance estimation. The specific case is utilized to discuss the ability of gross errors identification and the effect of prediction regression of this method. Compared with the traditional gross errors identification methods, this method has a higher identification accuracy and stronger robustness; compared with traditional neural network prediction methods, it has the advantages of fast learning speed, strong generalization ability and high prediction accuracy.