Evaluation of uncertainties associated with landslide displacement prediction is essential for improving the reliability of landslide early warning systems. An efficient probabilistic forecasting method for the construction of prediction intervals (PIs) using bootstrap and kernel-based extreme learning machine (ELM) is proposed. To overcome the drawbacks of artificial neural networks (ANNs) in predicting mutational displacement points with time lags, this paper proposes an ANNs switched prediction scheme to construct PIs with a three-stage formulation. In the first stage, K-means clustering is applied to divide the whole training dataset into two sub-training sets: the stationary points and the mutational points. In the second stage, a weighted ELM classifier is applied to construct the switched rules. In the third stage, bootstrap-and kernel-based ELMs are applied to construct candidate PIs for each sub-training set. The final PIs are constructed by switching between these two candidate PIs. The effectiveness of the proposed ANNs switched prediction method has been validated through comprehensive tests using three real-world landslide datasets from the Three Gorges region of China.