62 citations as recorded by crossref.

1. Kinetic Analysis of Rainfall-Induced Landslides in May 2022 in Wuping, Fujian, SE China T. Wang et al. 10.3390/w16213018
2. Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model Z. Lin et al. 10.3390/math10132203
3. A comparative study of different machine learning methods for reservoir landslide displacement prediction Y. Wang et al. 10.1016/j.enggeo.2022.106544
4. Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction Q. Ge et al. 10.3390/w16152152
5. Interval prediction of landslide displacement with dual-output least squares support vector machine and particle swarm optimization algorithms W. Gong et al. 10.1007/s11440-022-01455-2
6. Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China J. Li et al. 10.1007/s11069-024-06937-y
7. Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique J. Ma et al. 10.3390/ijerph17134788
8. A Hybrid Machine Learning Model Coupling Double Exponential Smoothing and ELM to Predict Multi-Factor Landslide Displacement X. Zhu et al. 10.3390/rs14143384
9. Early identification on failure mode of loess landslide: insight from case study and physical model experiment R. Li et al. 10.3389/feart.2024.1504864
10. A comparative evaluation of clustering methods and data sampling techniques in the prediction of reservoir landslide deformation state Q. Ge et al. 10.1080/17499518.2024.2341257
11. A hybrid intelligent approach for constructing landslide displacement prediction intervals Y. Wang et al. 10.1016/j.asoc.2019.105506
12. Forecasting slope deformation field using correlated grey model updated with time correction factor and background value optimization W. Zhang et al. 10.1016/j.enggeo.2019.105215
13. Input-parameter optimization using a SVR based ensemble model to predict landslide displacements in a reservoir area – A comparative study J. Zhang et al. 10.1016/j.asoc.2023.111107
14. Displacement prediction of landslides at slope-scale: Review of physics-based and data-driven approaches W. Gong et al. 10.1016/j.earscirev.2024.104948
15. Multiple data-driven approach for predicting landslide deformation S. Li et al. 10.1007/s10346-019-01320-6
16. A Hybrid Random Forest and Least Squares Support Vector Machine Model Based on Particle Swarm Optimization Algorithm for Slope Stability Prediction: A Case Study in Sichuan–Tibet Highway, China T. Shu et al. 10.1155/2023/6651323
17. A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine S. Zheng et al. 10.32604/cmes.2021.016761
18. Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China Y. Wang et al. 10.1155/2019/8985325
19. A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction X. Niu et al. 10.3390/app11104684
20. Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy A. van Natijne et al. 10.5194/nhess-23-3723-2023
21. Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method H. Du et al. 10.1016/j.jclepro.2020.122248
22. Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm L. Zhang et al. 10.3390/en12010196
23. Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China W. Wang et al. 10.1007/s11069-019-03595-3
24. Landslide displacement forecasting using deep learning and monitoring data across selected sites L. Nava et al. 10.1007/s10346-023-02104-9
25. Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir D. Li et al. 10.1007/s11629-019-5470-3
26. Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model Z. Guo et al. 10.1007/s10346-019-01314-4
27. Machine learning methods for predicting the uniaxial compressive strength of the rocks: a comparative study T. Wen et al. 10.1007/s11707-024-1101-6
28. Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides Z. Zou et al. 10.3390/ijerph17176030
29. Relationship between river bank stability and hydrological processes using in situ measurement data G. Mentes 10.1556/24.62.2019.01
30. Landslide displacement prediction based on spatio-temporal association rule mining between target case and similar cases F. Dai et al. 10.1007/s00477-023-02504-2
31. Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study J. Ma et al. 10.1007/s10346-022-01923-6
32. Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence X. Zhang et al. 10.1007/s11069-024-06673-3
33. Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir B. Yang et al. 10.3390/s22041320
34. Prediction of landslide displacement with step-like curve using variational mode decomposition and periodic neural network Q. Liu et al. 10.1007/s10064-021-02136-2
35. Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide K. Liao et al. 10.1007/s10064-019-01598-9
36. Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review R. He et al. 10.1016/j.rockmb.2024.100144
37. Short-Term Rockburst Damage Assessment in Burst-Prone Mines: An Explainable XGBOOST Hybrid Model with SCSO Algorithm Y. Qiu & J. Zhou 10.1007/s00603-023-03522-w
38. A Prediction Model of Hydrodynamic Landslide Evolution Process Based on Deep Learning Supported by Monitoring Big Data R. Wang et al. 10.3389/feart.2022.829221
39. 机器学习在滑坡智能防灾减灾中的应用与发展趋势 J. Dou et al. 10.3799/dqkx.2022.419
40. Innovative consideration of a directional appraisal parameter as destabilized criterion of the colluvial landslide H. Keqiang et al. 10.1007/s12665-019-8709-y
41. Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model Z. Lin et al. 10.3390/ijerph19042077
42. Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model W. Jia et al. 10.3390/w15040612
43. Forecasting step-like landslide displacement through diverse monitoring frequencies F. Guo et al. 10.1007/s11629-024-9064-3
44. Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability S. Dai et al. 10.3390/su10040958
45. A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression J. Ma et al. 10.1007/s00477-022-02183-5
46. Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors J. Wang et al. 10.1007/s12517-020-05452-1
47. Displacement prediction of tunnel entrance slope based on LSSVM and bacterial foraging optimization algorithm W. Xihao et al. 10.1038/s41598-024-75804-4
48. Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach J. Ma et al. 10.1155/2020/2624547
49. Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations Z. Ma & G. Mei 10.1016/j.jrmge.2024.02.034
50. A model for interpreting the deformation mechanism of reservoir landslides in the Three Gorges Reservoir area, China Z. Zou et al. 10.5194/nhess-21-517-2021
51. Dynamics of creeping landslides controlled by inelastic hydro-mechanical couplings X. Li et al. 10.1016/j.enggeo.2023.107078
52. A hybrid interval displacement forecasting model for reservoir colluvial landslides with step-like deformation characteristics considering dynamic switching of deformation states L. Li et al. 10.1007/s00477-020-01914-w
53. Landslide displacement prediction by using Bayesian optimization–temporal convolutional networks J. Yang et al. 10.1007/s11440-023-02205-8
54. Deformation prediction of reservoir landslides based on a Bayesian optimized random forest-combined Kalman filter N. Zhang et al. 10.1007/s12665-022-10317-9
55. A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm S. Zheng et al. 10.1155/2020/3897215
56. Slope stability prediction based on adaptive CE factor quantum behaved particle swarm optimization-least-square support vector machine J. Yang 10.3389/feart.2023.1098872
57. A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area J. Zhang et al. 10.3390/s20154287
58. Displacement Prediction of Channel Slope Based on EEMD-IESSA-LSSVM Combined Algorithm H. Yao et al. 10.3390/app13179582
59. Hydrodynamic landslide displacement prediction using combined extreme learning machine and random search support vector regression model R. Wang et al. 10.1080/19648189.2020.1754298
60. Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines Y. Wang et al. 10.1155/2020/7082594
61. An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China X. Xia et al. 10.1016/j.envres.2023.117143
62. Prediction of strata settlement in undersea metal mining based on deep forest W. Liu et al. 10.1038/s41598-024-80025-w