Improving ICEWS models: Forecasting SOUTHCOM events of interest using ensemble methods

2012 ◽  
pp. 455-464
Keyword(s):  
Ensemble Methods

scholarly journals Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Zhice Fang ◽  
Yi Wang ◽  
Gonghao Duan ◽  
Ling Peng
Keyword(s):  
Decision Trees ◽  
Landslide Susceptibility ◽  
Ensemble Methods ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Rotation Forest ◽  
Predictive Values ◽  
Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran

2021 ◽  
Author(s):  
Hamid Reza Pourghasemi ◽  
Fatemeh Honarmandnejad ◽  
Mahrooz Rezaei ◽  
Mohammad Hassan Tarazkar ◽  
Nitheshnirmal Sadhasivam
Keyword(s):  
Ensemble Methods ◽  
Water Erosion ◽  
Southern Iran

scholarly journals Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

scholarly journals Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 830
Author(s):  
William E. Lewis ◽  
Timothy L. Olander ◽  
Christopher S. Velden ◽  
Christopher Rozoff ◽  
Stefano Alessandrini
Keyword(s):  
North Atlantic ◽  
Mean Absolute Error ◽  
Ensemble Methods ◽  
Absolute Error ◽  
Eastern Pacific ◽  
Intensity Maximum ◽  
The North ◽  
The University ◽  
Meteorological Satellite ◽  
Fold Cross Validation

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

scholarly journals Machine learning applied to simulations of collisions between rotating, differentiated planets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Miles L. Timpe ◽  
Maria Han Veiga ◽  
Mischa Knabenhans ◽  
Joachim Stadel ◽  
Stefano Marelli
Keyword(s):  
Machine Learning ◽  
Planet Formation ◽  
Critical Role ◽  
Ensemble Methods ◽  
Terrestrial Planet ◽  
Data Driven ◽  
Analytic Methods ◽  
Accurate Treatment ◽  
Post Impact ◽  
The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

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