Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model
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- Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
- Li, Shaohong & Wu, Na, 2021. "A new grey prediction model and its application in landslide displacement prediction," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
- Zian Lin & Xiyan Sun & Yuanfa Ji, 2022. "Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
- Yong-gang Zhang & Jun Tang & Zheng-ying He & Junkun Tan & Chao Li, 2021. "A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 783-813, January.
- Zian Lin & Yuanfa Ji & Weibin Liang & Xiyan Sun, 2022. "Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model," Mathematics, MDPI, vol. 10(13), pages 1-19, June.
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Cited by:
- Jeong-Cheol Kim & Sunmin Lee, 2023. "Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea," Sustainability, MDPI, vol. 16(1), pages 1-13, December.
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Keywords
land use management; landslide displacement prediction; complete ensemble empirical mode decomposition with adaptive noise; bidirectional long short-term memory;All these keywords.
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