103 citations as recorded by crossref.

1. Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model D. Huang et al. https://doi.org/10.3390/rs14112656
2. Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China F. Ren et al. https://doi.org/10.1007/s12665-014-3764-x
3. Landslide susceptibility mapping and management in Western Serbia: an analysis of ANFIS- and SVM-based hybrid models I. Elkhrachy et al. https://doi.org/10.3389/fenvs.2023.1218954
4. A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping K. Kamran et al. https://doi.org/10.1007/s12518-021-00393-0
5. Displacement prediction of landslides at slope-scale: Review of physics-based and data-driven approaches W. Gong et al. https://doi.org/10.1016/j.earscirev.2024.104948
6. LEAST SQUARES SUPPORT VECTOR MACHINE MODEL FOR COORDINATE TRANSFORMATION Y. Ziggah et al. https://doi.org/10.3846/gac.2019.6053
7. Soft computing in assessment of earthquake-triggered landslide susceptibility C. Xu et al. https://doi.org/10.1007/s12665-016-5576-7
8. Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU H. Zheng et al. https://doi.org/10.1155/2022/4659853
9. An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest X. Xiao et al. https://doi.org/10.1080/19475705.2024.2347421
10. Development of hybrid machine learning-based carbonation models with weighting function Z. Chen et al. https://doi.org/10.1016/j.conbuildmat.2022.126359
11. A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management X. Sun et al. https://doi.org/10.1016/j.jenvman.2022.116108
12. Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation Y. Jiang et al. https://doi.org/10.3390/rs8030179
13. Estimating the rainfall threshold of a deep-seated landslide by integrating models for predicting the groundwater level and stability analysis of the slope Z. Wei et al. https://doi.org/10.1016/j.enggeo.2019.02.026
14. A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction X. Zhu et al. https://doi.org/10.1007/s11629-016-4245-3
15. Developing new tree expression programing and artificial bee colony technique for prediction and optimization of landslide movement Z. Luo et al. https://doi.org/10.1007/s00366-019-00754-9
16. Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach T. Oyehan et al. https://doi.org/10.1016/j.compbiomed.2018.04.024
17. Ensemble learning for landslide displacement prediction: A perspective of Bayesian optimization and comparison of different time series analysis methods L. Liu et al. https://doi.org/10.1007/s00477-024-02730-2
18. A new method for displacement prediction of “step-like” landslides based on VMD-FOA-SVR model X. Lu et al. https://doi.org/10.1007/s12665-021-09825-x
19. A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities B. Choubin et al. https://doi.org/10.1016/j.asr.2024.11.005
20. Effective and Accelerated Forewarning of Landslides Using Wireless Sensor Networks and Machine Learning T. Hemalatha et al. https://doi.org/10.1109/JSEN.2019.2928358
21. Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy) A. Corsini & M. Mulas https://doi.org/10.1007/s10346-016-0781-8
22. Overall grouting compactness detection of bridge prestressed bellows based on RF feature selection and the GA-SVM model H. Liu et al. https://doi.org/10.1016/j.conbuildmat.2021.124323
23. 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. https://doi.org/10.1155/2023/6651323
24. A Novel Hybrid LMD–ETS–TCN Approach for Predicting Landslide Displacement Based on GPS Time Series Analysis W. Luo et al. https://doi.org/10.3390/rs15010229
25. Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation J. Wang et al. https://doi.org/10.5194/nhess-19-1387-2019
26. Understanding the Mechanisms of Earth Fissuring for Hazard Mitigation in Najran, Saudi Arabia M. Alsaiari et al. https://doi.org/10.3390/su15076006
27. 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. https://doi.org/10.3390/s20154287
28. 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. https://doi.org/10.1007/s00477-022-02183-5
29. Short-Term Prediction of Vertical Deformation in Tidal Flat Terrains Based on PSO-VMD-LSTM Q. Zhou et al. https://doi.org/10.1109/TIM.2024.3413180
30. FEASIBILITY OF USING GROUP METHOD OF DATA HANDLING (GMDH) APPROACH FOR HORIZONTAL COORDINATE TRANSFORMATION B. Kumi-Boateng & Y. Ziggah https://doi.org/10.3846/gac.2020.10486
31. Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches X. Huang et al. https://doi.org/10.3390/ma15134436
32. Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis B. Durkaya Kurtcan & T. Ozcan https://doi.org/10.1002/for.2960
33. Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM Z. Huo et al. https://doi.org/10.3390/su15043631
34. Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach J. Ma et al. https://doi.org/10.1155/2020/2624547
35. Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm D. Yang et al. https://doi.org/10.1016/j.mechmachtheory.2015.03.013
36. Life-Cycle Performance Assessment and Distress Prediction of Subgrade Based on an Analytic Hierarchy Process and the PSO–LSSVM Model Q. Li et al. https://doi.org/10.3390/app10217529
37. Prediction of landslide displacement with dynamic features using intelligent approaches Y. Zhang et al. https://doi.org/10.1016/j.ijmst.2022.02.004
38. Slope displacement prediction using sequential intelligent computing algorithms C. Liu et al. https://doi.org/10.1016/j.measurement.2018.10.094
39. Comparative study on credit card fraud detection based on different support vector machines C. Li et al. https://doi.org/10.3233/IDA-195011
40. Data Mining and Deep Learning for Predicting the Displacement of “Step-like” Landslides F. Miao et al. https://doi.org/10.3390/s22020481
41. Application of a two-step cluster analysis and the Apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China X. Wu et al. https://doi.org/10.1007/s12665-015-5022-2
42. Utilization of Support Vector Models and Gene Expression Programming for Soil Strength Modeling A. Tenpe & A. Patel https://doi.org/10.1007/s13369-020-04441-6
43. Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review R. He et al. https://doi.org/10.1016/j.rockmb.2024.100144
44. Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping Z. Miao et al. https://doi.org/10.3390/app12031760
45. Displacement prediction model for rainfall-induced landslides based on PDL-VMD-SVR: A case study of the southern slope landslide at Fushun West Open-pit Mine, China M. Zhang et al. https://doi.org/10.1016/j.enggeo.2026.108855
46. A hybrid machine learning and computing model for forecasting displacement of multifactor-induced landslides X. Zhu et al. https://doi.org/10.1007/s00521-017-2968-x
47. Input-parameter optimization using a SVR based ensemble model to predict landslide displacements in a reservoir area – A comparative study J. Zhang et al. https://doi.org/10.1016/j.asoc.2023.111107
48. Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification Y. Guo et al. https://doi.org/10.3390/app13063718
49. A Cuckoo Search-Support Vector Machine Model for Predicting Dynamic Measurement Errors of Sensors M. Jiang et al. https://doi.org/10.1109/ACCESS.2016.2605041
50. The effectiveness of a drainage tunnel in increasing the rainfall threshold of a deep-seated landslide Z. Wei et al. https://doi.org/10.1007/s10346-019-01241-4
51. Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China Y. Fang et al. https://doi.org/10.2166/ws.2016.097
52. A semantic information-driven stepwise landslide displacement prediction model F. Tang et al. https://doi.org/10.1007/s10661-022-10417-w
53. Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River, India A. Yadav et al. https://doi.org/10.1080/02626667.2018.1483581
54. Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy G. Wu et al. https://doi.org/10.1016/j.compag.2020.105475
55. Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking Y. Tao & J. Zhou https://doi.org/10.1016/j.compag.2017.09.019
56. A novel hybrid algorithm with static and dynamic models for air quality index forecasting X. Zhao et al. https://doi.org/10.1007/s11071-023-08552-1
57. Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR) Z. Liu et al. https://doi.org/10.1007/s11069-023-06322-1
58. Prediction of landslide displacement based on GA-LSSVM with multiple factors Z. Cai et al. https://doi.org/10.1007/s10064-015-0804-z
59. SVM‐based hybrid approach for corridor‐level travel‐time estimation R. Sharmila et al. https://doi.org/10.1049/iet-its.2018.5069
60. Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM C. Yu & J. Chen https://doi.org/10.3390/sym12061047
61. A FRAMEWORK FOR FINGERPRINT LIVENESS DETECTION USING SUPPORT VECTOR MACHINE OPTIMIZED BY GENETIC ALGORITHM Y. IBRAHIM et al. https://doi.org/10.26634/jpr.5.2.15536
62. Predicting the Deformation of a Slope Using a Random Coefficient Panel Data Model Z. Yuan et al. https://doi.org/10.3390/fractalfract8070429
63. Comparison of LR, 5-CV SVM, GA SVM, and PSO SVM for landslide susceptibility assessment in Tibetan Plateau area, China Y. Zhang et al. https://doi.org/10.1007/s11629-022-7685-y
64. Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning L. Liu et al. https://doi.org/10.1016/j.gsf.2023.101758
65. Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran M. Asadi et al. https://doi.org/10.1007/s13201-025-02361-0
66. Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model I. Alade et al. https://doi.org/10.1016/j.nanoso.2018.12.001
67. Prediction of Slope Stability using Naive Bayes Classifier X. Feng et al. https://doi.org/10.1007/s12205-018-1337-3
68. A hybrid intelligent optimization approach to improving quality for serial multistage and multi-response coal preparation production systems X. Yin et al. https://doi.org/10.1016/j.jmsy.2018.05.006
69. A review of models for water level forecasting based on machine learning W. Wee et al. https://doi.org/10.1007/s12145-021-00664-9
70. Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study J. Ma et al. https://doi.org/10.1007/s10346-022-01923-6
71. Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches S. Sithara et al. https://doi.org/10.1007/s11600-020-00484-3
72. Hierarchical perception guided deep learning framework for landslide detection with multi-source remotely sensed data C. Zhong et al. https://doi.org/10.1080/17538947.2026.2632415
73. Rainfall threshold for initiation of channelized debris flows in a small catchment based on in-site measurement Z. Wei et al. https://doi.org/10.1016/j.enggeo.2016.12.003
74. Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset W. Xu et al. https://doi.org/10.3390/su14116908
75. Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach B. Zheng et al. https://doi.org/10.1155/2023/5525793
76. Intelligent optimization of Ti6Al4V alloy surface photolithography parameters via machine learning: A data-driven framework for precisionmanufacturing in industrial applications J. Lin et al. https://doi.org/10.1016/j.matchemphys.2025.131388
77. 考虑注意力机制的新型深度学习模型预测滑坡位移 Z. Guo et al. https://doi.org/10.3799/dqkx.2022.306
78. Prediction of Thermal Barrier Coatings Microstructural Features Based on Support Vector Machine Optimized by Cuckoo Search Algorithm D. Ye et al. https://doi.org/10.3390/coatings10070704
79. Determination of the shear failure areas of rock joints using a laser scanning technique and artificial intelligence algorithms Y. Ge et al. https://doi.org/10.1016/j.enggeo.2021.106320
80. Landslide Displacement Prediction Based on the VMD–TCN Hybrid Model C. Xu et al. https://doi.org/10.1007/s11204-025-10057-5
81. Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine M. Jiang et al. https://doi.org/10.1016/j.scs.2017.08.004
82. Assessment of circular-bored twin tunnel (CBTT) performance using soft computing methods H. Li et al. https://doi.org/10.1007/s00366-021-01288-9
83. Prediction of Slope Stability Based on Hybrid PSO and LSSVM X. Xue https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607
84. Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines B. Hou et al. https://doi.org/10.1109/ACCESS.2020.2995581
85. Predicting wave forces on coastal bridges using genetic algorithm enhanced ensemble learning framework G. Xu et al. https://doi.org/10.1016/j.oceaneng.2022.112963
86. Statistical downscaling of sea levels: application of multi-criteria analysis for selection of global climate models S. Sithara et al. https://doi.org/10.1007/s10661-022-10449-2
87. An improved SVM web page classification algorithm X. Ren et al. https://doi.org/10.1088/1742-6596/1187/4/042063
88. Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm – support vector regression model I. Alade et al. https://doi.org/10.1016/j.cmpb.2018.05.029
89. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran H. Pourghasemi & N. Kerle https://doi.org/10.1007/s12665-015-4950-1
90. Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning X. Huang et al. https://doi.org/10.3390/ma14154068
91. Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation A. Falamarzi et al. https://doi.org/10.1051/matecconf/201925902007
92. Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China C. Zhou et al. https://doi.org/10.1016/j.enggeo.2016.02.009
93. Landslide Displacement Prediction Method Based on GA-Elman Model C. Wang et al. https://doi.org/10.3390/app112211030
94. A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams H. Nezaratian et al. https://doi.org/10.2166/wqrj.2021.003
95. Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection F. Dong et al. https://doi.org/10.1016/j.energy.2023.130200
96. Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan Province, China X. Zhu et al. https://doi.org/10.1016/j.enggeo.2017.01.022
97. Intelligent identification method for pipeline ultrasonic internal inspection X. Guangli et al. https://doi.org/10.1080/10589759.2024.2389935
98. Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm J. Yuan et al. https://doi.org/10.1093/fqsafe/fyac071
99. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping H. Hong et al. https://doi.org/10.1016/j.scitotenv.2020.140549
100. Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China) H. Hong et al. https://doi.org/10.1080/19475705.2016.1250112
101. Discharge modeling and characteristic analysis of semi-circular side weir based on the soft computing method S. Li et al. https://doi.org/10.2166/hydro.2023.268
102. Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification X. Sun et al. https://doi.org/10.1007/s10064-020-01849-0
103. MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning A. Guo et al. https://doi.org/10.1109/JSTARS.2023.3338449