Articles | Volume 14, issue 3
https://doi.org/10.5194/nhess-14-525-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-14-525-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Application of GA–SVM method with parameter optimization for landslide development prediction
X. Z. Li
Key Laboratory of Mountain Hazards and Surface Process, Chinese Academy of Sciences, 610041, Chengdu, China
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, 610041, Chengdu, China
J. M. Kong
Key Laboratory of Mountain Hazards and Surface Process, Chinese Academy of Sciences, 610041, Chengdu, China
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, 610041, Chengdu, China
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- 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
- Prediction of Slope Stability using Naive Bayes Classifier X. Feng et al. 10.1007/s12205-018-1337-3
- 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
- A review of models for water level forecasting based on machine learning W. Wee et al. 10.1007/s12145-021-00664-9
- Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study J. Ma et al. 10.1007/s10346-022-01923-6
- 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
- Rainfall threshold for initiation of channelized debris flows in a small catchment based on in-site measurement Z. Wei et al. 10.1016/j.enggeo.2016.12.003
- 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
- Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach B. Zheng et al. 10.1155/2023/5525793
- 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. 10.3799/dqkx.2022.306
- Prediction of Thermal Barrier Coatings Microstructural Features Based on Support Vector Machine Optimized by Cuckoo Search Algorithm D. Ye et al. 10.3390/coatings10070704
- Determination of the shear failure areas of rock joints using a laser scanning technique and artificial intelligence algorithms Y. Ge et al. 10.1016/j.enggeo.2021.106320
- Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine M. Jiang et al. 10.1016/j.scs.2017.08.004
- Assessment of circular-bored twin tunnel (CBTT) performance using soft computing methods H. Li et al. 10.1007/s00366-021-01288-9
- Prediction of Slope Stability Based on Hybrid PSO and LSSVM X. Xue 10.1061/(ASCE)CP.1943-5487.0000607
- Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines B. Hou et al. 10.1109/ACCESS.2020.2995581
- Predicting wave forces on coastal bridges using genetic algorithm enhanced ensemble learning framework G. Xu et al. 10.1016/j.oceaneng.2022.112963
- Statistical downscaling of sea levels: application of multi-criteria analysis for selection of global climate models S. Sithara et al. 10.1007/s10661-022-10449-2
- An improved SVM web page classification algorithm X. Ren et al. 10.1088/1742-6596/1187/4/042063
- 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
- 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
- Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning X. Huang et al. 10.3390/ma14154068
- Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation A. Falamarzi et al. 10.1051/matecconf/201925902007
- Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China C. Zhou et al. 10.1016/j.enggeo.2016.02.009
- Landslide Displacement Prediction Method Based on GA-Elman Model C. Wang et al. 10.3390/app112211030
- A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams H. Nezaratian et al. 10.2166/wqrj.2021.003
- 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
- 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
- Intelligent identification method for pipeline ultrasonic internal inspection X. Guangli et al. 10.1080/10589759.2024.2389935
- Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm J. Yuan et al. 10.1093/fqsafe/fyac071
- 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. 10.1016/j.scitotenv.2020.140549
- 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. 10.1080/19475705.2016.1250112
- 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
- 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
- MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning A. Guo et al. 10.1109/JSTARS.2023.3338449
4 citations as recorded by crossref.
- A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data Z. Ma et al. 10.1007/s00521-021-06084-6
- Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study) R. Mahalingam et al. 10.1080/19475705.2016.1172520
- Susceptibility Assessment of Landslides Triggered by the Lushan Earthquake, April 20, 2013, China R. Niu et al. 10.1109/JSTARS.2014.2308553
- Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region M. Rana & C. Mahanta 10.1007/s11069-022-05580-9
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