针对支持向量机在滑坡位移预测中输入项权值无差异，从而影响模型预测精度的问题，提出一种基于CRITIC（criteria importance though intercrieria correlation）赋权法与粒子群优化算法（particle swarm optimization, PSO）支持向量回归机（support vector regression, SVR）的滑坡位移预测模型。该模型首先采用皮尔逊相关性分析法，选取与模型输出项相关性较强的三项影响因素，然后由CRITIC赋权法求得对应权值，将加权后的训练集输入基于CRITIC赋权法与PSO-SVR的预测模型，以实现对滑坡地表位移的预测。结果表明：相比SVR、PSO-SVR以及基于熵权法与PSO-SVR的预测模型，本模型具有良好的泛化能力，均方根误差和判定系数分别比未赋权模型降低38.24%和提高6.64%，能有效提高预测精度，预测效果优于其他对比模型。

In order to address the issue of no difference in input weight values in landslide displacement prediction, which affected the accuracy of the model prediction, a landslide displacement prediction model was proposed based on CRITIC (criterion importance through intercriteria correlation) weighting method and particle swarm optimization (PSO) algorithm to optimize support vector regression (SVR). The model first used Pearson correlation analysis to select three influencing factors with strong correlation with the model output. Then, the CRITIC weighting method was used to obtain the corresponding weights, and the weighted training set was input into a prediction model based on CRITIC weighting method and PSO-SVR to achieve the prediction of landslide surface displacement. The results show that compared with SVR, PSO-SVR, and prediction models based on entropy weighting and PSO-SVR, this model has good generalization ability. The root mean square error and decision coefficient are reduced by 38.24% and increased by 6.64% compared to the unweighted model, respectively, which can effectively improve prediction accuracy. The prediction effect is better than other comparison models.