Open Access

ARTICLE

Prediction of Landslide Displacement Using a BiLSTM-RBF Model Based on a Hybrid Attention Mechanism

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

1 College of Electrical Engineering, Guizhou University, Guiyang, 550025, China
2 College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

* Corresponding Author: Xiao Wang. Email:

Computers, Materials & Continua 2025, 85(3), 5423-5450. https://doi.org/10.32604/cmc.2025.067952

Received 16 May 2025; Accepted 15 August 2025; Issue published 23 October 2025

Abstract

This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double Exponential Smoothing (DES) ensemble optimized through a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to enhance trend prediction; while employing a Bidirectional Long Short-Term Memory-Radial Basis Function (BiLSTM-RBF) network enhanced by a hybrid attention mechanism, which facilitates a global-local synergistic approach to hierarchical feature extraction, thereby improving the prediction of periodic displacements; (3) A bidirectional adaptive feature extraction mechanism aligns attention weights with BiLSTM propagation paths through spatial mapping, complemented by an innovative loss function incorporating Prediction Interval (PI) width optimization. In the comparative experiments of the Baishuihe landslide: the RMSE, MAE, and R² indexes of monitoring point ZG118 are improved by 19.8%, 35.2%, and 3.2% compared with the optimal baseline model (RBF-MIC); in the monitoring point ZG93, where the amount of data is less, the three indexes are even more improved by 52.1%, 32.3%, and 21.8% compared with the optimal baseline model (GRU-None). These results substantiate the model’s capacity to overcome dual constraints of data paucity and feature engineering limitations in geohazard prediction.

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