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An ensemble method based on weight voting method for improved prediction of slope stability
This study proposes a novel ensemble method based on weighted majority voting to evaluate the slope stability. The ensemble classifier is composed of...
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Uncertainty analysis method of slope safety factor based on quantile-based ensemble learning
To overcome the problem that the point prediction method of slope safety factor has uncertainty in its prediction and hence cannot a reliable slope...
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Application of a weighted ensemble forecasting method based on online learning in subseasonal forecast in the South China
Under the proposal of “seamless forecasting”, it has become a key problem for meteorologists to improve the skills of subseasonal forecasts. Since...
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GNSS NLOS detection method based on stacking ensemble learning and applications in smartphones
Global Satellite Navigation System (GNSS) has been widely used in various high-precision positioning services and has become an indispensable part of...
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Drought index downscaling using AI-based ensemble technique and satellite data
This study introduces and validates an artificial intelligence (AI)–based downscaling method for Standardized Precipitation Indices (SPI) in the...
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Runoff Forecasting of Machine Learning Model Based on Selective Ensemble
Reliable runoff forecasting plays an important role in water resource management. In this study, we propose a homogeneous selective ensemble...
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DenseNet-based ensemble network for land cover and land use classification of patch-based denoised SAR images
Classification of synthetic aperture radar (SAR) images is very important for analyzing these images. The develo** remote sensing allows many...
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Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model
In the ensemble Kalman filter (EnKF) framework for data assimilation, a limited ensemble size results in a spurious sampling error and...
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A stacked ensemble learning-based framework for mineral map** using AVIRIS-NG hyperspectral image
AbstractHyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at...
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Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms
Rockburst is a mine dynamic disaster caused by the rapid release of elastic strain energy of surrounding rock. As the depth of engineering project...
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A natural Hessian approximation for ensemble based optimization
A key challenge in reservoir management and other fields of engineering involves optimizing a nonlinear function iteratively. Due to the lack of...
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An ensemble-based data assimilation system for forecasting variability of the Northwestern Pacific ocean
An adjoint-free four-dimensional variational (a4dVar) data assimilation (DA) is implemented in an operational ocean forecast system based on an...
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Application of hybrid machine learning-based ensemble techniques for rainfall-runoff modeling
The main aim of this study was to develop hybrid machine learning (ML)-based ensemble modeling of the rainfall-runoff process in the Katar catchment,...
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Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery
The back propagation (BP) neural network method is widely used in bathymetry based on multispectral satellite imagery. However, the classical BP...
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A Recombination Clustering Technique for Forecasting of Tropical Cyclone Tracks Based on the CMA-TRAMS Ensemble Prediction System
Despite marked improvements in tropical cyclone (TC) track ensemble forecasting, forecasters still have difficulty in making quick decisions when...
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Incremental–decremental data transformation based ensemble deep learning model (IDT-eDL) for temperature prediction
Human life heavily depends on weather conditions, which affect the necessary operations like agriculture, aviation, tourism, industries, etc., where...
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A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation
Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme...
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Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles
We consider the implementation of ensemble-based Kalman filters (EnKF) in the framework of ensembles of different accuracies and sizes that are...
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Ensemble tree model prediction of summer precipitation in North China based on predictor selection strategy
Selection of predictors is a key issue in using machine learning (ML) models to perform short-term climate prediction, and it is also one of the main...
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Earthquake prediction from seismic indicators using tree-based ensemble learning
Earthquake prediction is a challenging research area, but the use of a variety of machine learning models, together with a range of seismic...
