Yuhan Luo
ABSTRACT
It is of great practical significance for landslide prediction, because its uncertainty has brought great harm to the safety of human life and property. This study proposes a new algorithm that combines Principal Component Analysis, Ensemble Empirical Mode Decomposition, Long Short-Term Memory Network which establishes a PCA-EEMD-LSTM combined model to predict the cumulative displacement of the landslide. Through PCA, it is found that temperature, relative humidity, and wind speed (east-west, north-south) are the important meteorological factors affecting the landslide in this case. Then we decompose the entire displacement into sub-sequences of different frequencies through EEMD, predict each sub-sequence separately through LSTM, and finally reconstruct all sub-sequence predictions to get the final prediction result. We not only compare the difference between the predicted value of the model and the actual measured value, the accuracy of the four models of PCA-LSTM, EEMD-LSTM, ELM, and BP after training under the same data set conditions are also compared. The results show that the short-term prediction effect of the PCA-EEMD-LSTM model is better than other models. Compared with the conventional landslide prediction model, this model has a shorter time span, and has higher accuracy and stability, which is of great significance for landslide prediction.
- Beibei Yang,Kunlong Yin,Suzanne Lacasse, and Zhongqiang Liu. 2019. Time series analysis and long short-term memory neural network to predict landslide displacement.(4). https://doi.org/10.1007/s10346-018-01127-xGoogle Scholar
- Yankun Wang, Huiming Tang, Tao Wen, and Junwei Ma. 2019. A hybrid intelligent approach for constructing landslide displacement prediction intervals. Applied Soft Computing, vol. 81, pp. 105506. https://doi.org/10.1016/j.asoc.2019.105506Google ScholarDigital Library
- Wei Chen, Xusheng Yan, Zhou Zhao, Haoyuan Hong; Dieu Tien Bui, and Biswajeet Pradhan. 2019. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bulletin of Engineering Geology and the Environment(1). https://doi.org/10.1007/s10064-018-1256-zGoogle Scholar
- Chao Zhou, Kunlong Yin,Y ing Cao, Bayes Ahmed, and Xiaolin Fu. 2018. A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms. Scientific Reports(1). https://doi.org/10.1038/s41598-018-25567-6.Google Scholar
- Dong Van Dao, Abolfazl Jaafari, Mahmoud Bayat, and Davood Mafi-Gholami. 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena, vol. 188, pp. 104451. https://doi.org/10.1016/j.catena.2019.104451Google ScholarCross Ref
- Dieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, Ataollah Shirzadi, Marten Geertsema, and John J. Clague. 2019. Shallow landslide prediction using a novel hybrid functional machine learning algorithm,” Remote Sensing, vol. 11, no. 8, pp. 931. https://doi.org/10.3390/rs11080931Google ScholarCross Ref
- Daniel Granato, Jânio S. Santos, Graziela B. Escher, Bruno L. Ferreira, and Rubén M. Maggio. 2018. Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science & Technology, vol. 72, pp. 83-90. https://doi.org/10.1016/j.tifs.2017.12.006Google ScholarCross Ref
- Yagang Zhang, Bing Chen, Guifang Pan, and Yuan Zhao. 2019. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Conversion and Management, vol. 195, pp. 180-197. https://doi.org/10.1016/j.enconman.2019.05.005Google ScholarCross Ref
- Muhammad Basharat, Abid Ali, Ishtiaq A. K. Jadoon, and Joachim Rohn. 2016. Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region, NW Himalayas, Pakistan. Natural Hazards, vol. 81, no. 3, pp. 1999-2017.Google ScholarCross Ref
- Yaguo Lei, Zhengjia He, and Yanyang. Zi. 2009. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1327-1338. https://doi.org/10.1016/j.ymssp.2008.11.005Google ScholarCross Ref
- Qiao-Feng Tan, Xiao-Hui Lei, Xu Wang, Hao Wang, Xin Wen, Yi Ji, and Ai-Qin Kang. 2018. An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology, vol. 567, pp. 767-780. https://doi.org/10.1016/j.jhydrol.2018.01.015Google ScholarCross Ref
- Tong Wang, Mingcai Zhang, Qihao Yu, and Huyuan Zhang. 2012. Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. Journal of Applied Geophysics, vol. 83, pp. 29-34. https://doi.org/10.1016/j.jappgeo.2012.05.002Google ScholarCross Ref
- Hua Li, Tao Liu, Xing Wu, and Qing Chen. 2019. Application of EEMD and improved frequency band entropy in bearing fault feature extraction. ISA transactions, vol. 88, pp. 170-185. https://doi.org/10.1016/j.isatra.2018.12.002Google Scholar
- Qiang Fu, Bo Jing, Pengju He, Shuhao Si, and Yun Wang. 2018. Fault feature selection and diagnosis of rolling bearings based on EEMD and optimized Elman_AdaBoost algorithm. IEEE Sensors Journal, vol. 18, no. 12, pp. 5024-5034. https://doi.org/10.1109/JSEN.2018.2830109Google ScholarCross Ref
- Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2016. LSTM: A search space odyssey,” IEEE transactions on neural networks and learning systems, vol. 28, no. 10, pp. 2222-2232. https://doi.org/10.1109/TNNLS.2016.2582924Google Scholar
- Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991.Google Scholar
- Zheng Zhao, Weihai Chen, Xingming Wu, Peter C. Chen, and Jingmeng Liu. 2016. LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68-75. https://doi.org/10.1049/iet-its.2016.0208Google ScholarCross Ref
- Shiluo Xu, and Ruiqing Niu. 2018. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Computers & Geosciences, vol. 111, pp. 87-96. https://doi.org/10.1016/j.cageo.2017.10.013Google ScholarDigital Library
- Liming Xiao, Yonghong Zhang, and Gongzhuang Peng. 2018. Landslide susceptibility assessment using integrated deep learning algorithm along the China-Nepal highway。 Sensors, vol. 18, no. 12, pp. 4436, 2018. https://doi.org/10.3390/s18124436Google Scholar
- A novel hybrid model based on PCA-EEMD-LSTM neural network for short-term landslide prediction
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