Landslide displacement prediction model based on optimal decomposition and deep attention mechanism

Ren, Shuai and Kamarul Hawari, Ghazali and Ji, Yuanfa and Khan, Samra Urooj (2025) Landslide displacement prediction model based on optimal decomposition and deep attention mechanism. IEEE Access, 13. 51573 -51588. ISSN 2169-3536. (Published)

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Abstract

Landslide displacement forecasting is crucial for disaster prevention and risk management, as it enables timely warnings and effective mitigation strategies. However, the highly nonlinear and complex nature of landslide displacement poses significant challenges for accurate prediction. To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search Algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. The trend component is modeled using the Autoregressive Integrated Moving Average (ARIMA) with a grid search strategy, while the periodic component is predicted using a Bidirectional Long Short-Term Memory network with an Attention mechanism (BiLSTM-Attention), which dynamically adjusts the contribution of influencing factors. Grey Relational Analysis (GRA) is further employed to identify key external driving factors, enhancing prediction accuracy. Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. Moreover, the Mean Absolute Scaled Error (MASE) analysis confirms the robustness of the model in capturing both short-term fluctuations and long-term trends. Given its superior predictive accuracy and practical applicability, this approach provides valuable technical support for landslide monitoring and early warning systems.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Attention mechanism; Bidirectional long short term memory network; Intelligent optimization algorithm; Landslide displacement prediction; Variational mode decomposition
Subjects:	Q Science > QE Geology T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs. Nurul Hamira Abd Razak
Date Deposited:	30 Apr 2025 07:23
Last Modified:	30 Apr 2025 07:23
URI:	http://umpir.ump.edu.my/id/eprint/44455
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