96 citations as recorded by crossref.

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30. Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM Z. Huo et al. 10.3390/su15043631
31. 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
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38. 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. 10.1007/s12665-015-5022-2
39. Utilization of Support Vector Models and Gene Expression Programming for Soil Strength Modeling A. Tenpe & A. Patel 10.1007/s13369-020-04441-6
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43. Application of Support Vector Machine Algorithm Incorporating Slime Mould Algorithm Strategy in Ancient Glass Classification Y. Guo et al. 10.3390/app13063718
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47. A semantic information-driven stepwise landslide displacement prediction model F. Tang et al. 10.1007/s10661-022-10417-w
48. Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River, India A. Yadav et al. 10.1080/02626667.2018.1483581
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51. A novel hybrid algorithm with static and dynamic models for air quality index forecasting X. Zhao et al. 10.1007/s11071-023-08552-1
52. Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine X. Li & S. Li 10.3390/app11041381
53. Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR) Z. Liu et al. 10.1007/s11069-023-06322-1
54. Prediction of landslide displacement based on GA-LSSVM with multiple factors Z. Cai et al. 10.1007/s10064-015-0804-z
55. SVM‐based hybrid approach for corridor‐level travel‐time estimation R. Sharmila et al. 10.1049/iet-its.2018.5069
56. Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM C. Yu & J. Chen 10.3390/sym12061047
57. A FRAMEWORK FOR FINGERPRINT LIVENESS DETECTION USING SUPPORT VECTOR MACHINE OPTIMIZED BY GENETIC ALGORITHM Y. IBRAHIM et al. 10.26634/jpr.5.2.15536
58. Comparison of LR, 5-CV SVM, GA SVM, and PSO SVM for landslide susceptibility assessment in Tibetan Plateau area, China Y. Zhang et al. 10.1007/s11629-022-7685-y
59. Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning L. Liu et al. 10.1016/j.gsf.2023.101758
60. 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. 10.1016/j.nanoso.2018.12.001
61. Prediction of Slope Stability using Naive Bayes Classifier X. Feng et al. 10.1007/s12205-018-1337-3
62. A hybrid intelligent optimization approach to improving quality for serial multistage and multi-response coal preparation production systems X. Yin et al. 10.1016/j.jmsy.2018.05.006
63. A review of models for water level forecasting based on machine learning W. Wee et al. 10.1007/s12145-021-00664-9
64. Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study J. Ma et al. 10.1007/s10346-022-01923-6
65. Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches S. Sithara et al. 10.1007/s11600-020-00484-3
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67. Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset W. Xu et al. 10.3390/su14116908
68. Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach B. Zheng et al. 10.1155/2023/5525793
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78. Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm – support vector regression model I. Alade et al. 10.1016/j.cmpb.2018.05.029
79. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran H. Pourghasemi & N. Kerle 10.1007/s12665-015-4950-1
80. Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning X. Huang et al. 10.3390/ma14154068
81. Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation A. Falamarzi et al. 10.1051/matecconf/201925902007
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84. A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams H. Nezaratian et al. 10.2166/wqrj.2021.003
85. 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. 10.1016/j.energy.2023.130200
86. 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. 10.1016/j.enggeo.2017.01.022
87. Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm J. Yuan et al. 10.1093/fqsafe/fyac071
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90. Discharge modeling and characteristic analysis of semi-circular side weir based on the soft computing method S. Li et al. 10.2166/hydro.2023.268
91. 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. 10.1007/s10064-020-01849-0
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