scholarly journals A Random Forest Method to Forecast Downbursts Based on Dual-Polarization Radar Signatures

2019 ◽  
Vol 11 (7) ◽  
pp. 826 ◽  
Author(s):  
Bruno Medina ◽  
Lawrence Carey ◽  
Corey Amiot ◽  
Retha Mecikalski ◽  
William Roeder ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Method ◽  
The United States ◽  
Random Forest Model ◽  
Dual Polarization ◽  
Forest Model ◽  
Random Forest Method ◽  
Reliable Performance ◽  
Radar Signatures ◽  
Kennedy Space

The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events using a 35-knot wind threshold to separate these two categories. The downburst occurrence was assessed using a dense network of wind observations around CCAFS/KSC. Eight dual-polarization radar signatures that are hypothesized to have physical implications for downbursts at the surface were automatically calculated for 209 storms and ingested into the Random Forest model. The Random Forest model predicted null events more correctly than downburst events, with a True Skill Statistic of 0.40. Strong downburst events were better classified than those with weaker wind magnitudes. The most important radar signatures were found to be the maximum vertically integrated ice and the peak reflectivity. The Random Forest model presented a more reliable performance than an automated prediction method based on thresholds of single radar signatures. Based on these results, the Random Forest method is suggested for continued operational development and testing.

Study on Identification of 100% Cotton Fabric by Raman Spectroscopy and Random Forest

2014 ◽  
Vol 1033-1034 ◽  
pp. 439-443
Author(s):  
Jun Luo ◽  
Jian Fei Xie ◽  
Wei Fan
Keyword(s):  
Raman Spectroscopy ◽  
Random Forest ◽  
Cotton Fabric ◽  
Analytical Method ◽  
Random Forest Model ◽  
Laser Source ◽  
Forest Model ◽  
Raman Spectrometer ◽  
Random Forest Method ◽  
Training Samples

The analytical method was established for identification of 100% cotton fabric by Raman spectroscopy. 100 samples were analyzed directly by Raman spectrometer with a 1064nm laser source. 1120-1180 cm-1,1320-1400cm-1 and 1560-1600cm-1 were selected as important spectral regions by Random forest method. A Random forest model was established with 65 trees and 80 training samples. The result showed that different kind of textile can be identified by Raman spectroscopy coupled with random forest method.

scholarly journals Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lu Liu ◽  
Shushan Zhang ◽  
Xiaofei Yao ◽  
Hongmei Gao ◽  
Zhihua Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Success Rate ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Random Forest Model ◽  
Training Dataset ◽  
Liquefaction Potential ◽  
Forest Model ◽  
Random Forest Method

Liquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed using the Andrus database as a training dataset comprising 225 sets of liquefaction performance and shear wave velocity measurements. Five training parameters are selected for RF model including seismic magnitude ( M w ), peak horizontal ground surface acceleration ( a max ), stress-corrected shear wave velocity of soil ( V s 1 ), sandy-layer buried depth (ds), and a new introduced parameter, stress ratio (k). In addition, the optimal hyperparameters for the random forest model are determined based on the minimum error rate for the out-of-bag dataset (ERROOB) such as the number of classification trees, maximum depth of trees, and maximum number of features. The established random forest model was validated using the Kayen database as testing dataset and compared with the Chinese code and the Andrus methods. The results indicated that the random forest method established based on the training dataset was credible. The random forest method gave a success rate for liquefied sites and even a total success rate for all cases higher than 80%, which is completely acceptable. By contrast, the Chinese code method and the Andrus methods gave a high success rate for liquefaction but very low for nonliquefaction which led to the increase of engineering cost. The developed RF model can provide references for engineers to evaluate liquefaction potential.

scholarly journals RANDOM FOREST ALGORITHM OPTIMIZATION OF ENTERPRISE FINANCIAL INFORMATION MANAGEMENT SYSTEM

2018 ◽  
Vol 48 (4) ◽  
pp. 255-260
Author(s):  
X. H. LIU ◽  
E. X. WANG ◽  
Y. Q. ZHENG
Keyword(s):  
Random Forest ◽  
Information Management ◽  
Financial Management ◽  
Management System ◽  
Prediction Method ◽  
Information Management System ◽  
Financial Information ◽  
Random Forest Model ◽  
Random Forest Algorithm ◽  
Forest Model

The optimization of random forest algorithms for enterprise financial information management systems is studied in this paper. A random forest algorithm was proposed to improve the data processing capabilities of the financial system. This paper proposes a random forest model on the premise of referring to the latest results of machine learning. The algorithm was introduced into the real estate business financial management system in this paper. First, the samples are divided into training samples and test samples, and the direct prediction method and the two-step prediction method are applied. Mean SR and MAPE were used to compare the prediction accuracy of different algorithms and it was found that the direct prediction method is better. In the algorithm used in this paper, the random forest effect is the best. Then the linear regression, decision tree, neural network and random forest model fitting effects were compared and the best fitting degree of random forest was found.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model
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