Rolling speed is an important indicator for evaluating compaction quality, but in the monitoring process, the rolling speed is easily disturbed by construction environment, positioning drift and other disturbances, resulting in outliers which will affect the accuracy of compaction quality evaluation. However, methods for detecting and correcting outliers of rolling speed are not reported in current research. To ensure the data quality of the rolling speed, based on the characteristics of the time series of rolling speed, we adopted Kmeans algorithm to detect outliers preliminarily and weaken the influence of outliers on the results of empirical mode decomposition (EMD). According to the results of EMD, the fine quantitative detection of outliers was achieved, which in turn improved the accuracy of outlier detection. Furthermore, whale optimization algorithm (WOA) improved by chaos population initialization, nonlinear convergence factors, adaptive inertial weights and catfish effect-golden sine was used to optimize the Elman neural network and then a correction model was established for the correction of outliers of rolling speed. According to the application of the proposed method to a large hydropower project in Southwest China, the combined effect of Kmeans algorithm and EMD can detect outliers of rolling speed with higher accuracy than the box plot method; the correlation coefficient between the predicted value of the IWOA-Elman neural network and the actual value can reach up to 0.907 75, compared to conventional models, the IWOA-Elman neural network can not only ensure better data integrity and reliability, but also lay a good data foundation for the high-precision evaluation of compaction quality.