Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir
出版年份 2019 全文链接
标题
Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir
作者
关键词
-
出版物
Landslides
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-11-21
DOI
10.1007/s10346-019-01312-6
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models
- (2018) Huajin Li et al. Landslides
- Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched
- (2018) Cheng Lian et al. NEUROCOMPUTING
- Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death
- (2018) Daniel García Iglesias et al. SENSORS
- Wind Turbine Blade Breakage Monitoring With Deep Autoencoders
- (2018) Long Wang et al. IEEE Transactions on Smart Grid
- Performance Assessment of Wind Turbines: Data-Derived Quantitative Metrics
- (2018) Yusen He et al. IEEE Transactions on Sustainable Energy
- FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images
- (2018) Sarah E. Gerard et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Continuous Wavelet Analysis for Spectroscopic Determination of Subsurface Moisture and Water-Table Height in Northern Peatland Ecosystems
- (2017) Asim Banskota et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Auxiliary Hybrid PSO-BPNN-Based Transmission System Loss Estimation in Generation Scheduling
- (2017) C. H. Ram Jethmalani et al. IEEE Transactions on Industrial Informatics
- Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model
- (2017) Fasheng Miao et al. Landslides
- Deep belief network based deterministic and probabilistic wind speed forecasting approach
- (2016) H.Z. Wang et al. APPLIED ENERGY
- Multiple neural networks switched prediction for landslide displacement
- (2015) Cheng Lian et al. ENGINEERING GEOLOGY
- Mechanism of the slow-moving landslides in Jurassic red-strata in the Three Gorges Reservoir, China
- (2014) Haibo Miao et al. ENGINEERING GEOLOGY
- 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
- (2014) Fu Ren et al. Environmental Earth Sciences
- Quantitative risk analysis for landslides: the case of the Three Gorges area, China
- (2014) Ling Peng et al. Landslides
- Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece
- (2014) Konstantinos Chousianitis et al. SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
- Patterns of movement in reactivated landslides
- (2013) C.I. Massey et al. ENGINEERING GEOLOGY
- Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis
- (2013) Cheng Lian et al. NEURAL COMPUTING & APPLICATIONS
- A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
- (2012) Biswajeet Pradhan COMPUTERS & GEOSCIENCES
- Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine
- (2012) Cheng Lian et al. NATURAL HAZARDS
- Spectroscopic determination of leaf water content using continuous wavelet analysis
- (2010) T. Cheng et al. REMOTE SENSING OF ENVIRONMENT
- Landslide monitoring with high resolution tilt measurements at the Dollendorfer Hardt landslide, Germany
- (2009) A. García et al. GEOMORPHOLOGY
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