Abstract
Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.
Highlights
Landslides are a common natural hazard and cause extensive losses in mountainous areas
Extreme learning machine is an algorithm based on single-hidden layer feed-forward neural network, which has been reported with good prediction capacity[36,37,38,39,40]
Landslide total displacement can be decomposed into multi-level sub-sequences with various frequencies using the wavelet transform (WT)
Summary
Landslides are a common natural hazard and cause extensive losses in mountainous areas. Decomposition and support vector machine (SVM) to establish a displacement prediction method by considering the response relationship between triggering factors and landslide deformation[21]. These methods perform well and provide crucial parameters for EWS, they require a huge amount of monitoring data to analyze the deformation mechanism. A variety of computational intelligence methods have been applied in landslide study to achieve scientifically valid and accurate results[23,24,25,26,27,28] These methods can maximize the extraction of useful information from limited data for landslide displacement prediction. The artificial bee colony (ABC) is a swarm intelligence-based global optimization algorithm, and has been utilized to search the optimal parameters of KELM due to its high accuracy and fast convergence characteristic[42]
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