scholarly journals Consistency of survival tree and forest models: splitting bias and correction

2022 ◽  
Author(s):  
Yifan Cui ◽  
Ruoqing Zhu ◽  
Mai Zhou ◽  
Michael Kosorok
Keyword(s):  
Forest Models ◽  
Survival Tree

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces

2012 ◽  
Vol 8 (2) ◽  
pp. 44-63 ◽  
Author(s):  
Baoxun Xu ◽  
Joshua Zhexue Huang ◽  
Graham Williams ◽  
Qiang Wang ◽  
Yunming Ye
Keyword(s):  
Random Forest ◽  
High Dimensional Data ◽  
Real Life ◽  
Classification Performance ◽  
Feature Weighting ◽  
Random Forest Model ◽  
High Dimensional ◽  
Forest Model ◽  
Forest Models ◽  
Random Forest Models

The selection of feature subspaces for growing decision trees is a key step in building random forest models. However, the common approach using randomly sampling a few features in the subspace is not suitable for high dimensional data consisting of thousands of features, because such data often contains many features which are uninformative to classification, and the random sampling often doesn’t include informative features in the selected subspaces. Consequently, classification performance of the random forest model is significantly affected. In this paper, the authors propose an improved random forest method which uses a novel feature weighting method for subspace selection and therefore enhances classification performance over high-dimensional data. A series of experiments on 9 real life high dimensional datasets demonstrated that using a subspace size of features where M is the total number of features in the dataset, our random forest model significantly outperforms existing random forest models.

Risk Factors for Recurrence After Lung Cancer Resection as Estimated Using the Survival Tree Method

CHEST Journal ◽  
2013 ◽  
Vol 144 (4) ◽  
pp. 1238-1244 ◽  
Author(s):  
Shigeki Sawada ◽  
Natsumi Yamashita ◽  
Hiroshi Suehisa ◽  
Motohiro Yamashita
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Cancer Resection ◽  
Risk Factors For Recurrence ◽  
Survival Tree ◽  
Tree Method

scholarly journals Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

2013 ◽  
Vol 289 ◽  
pp. 255-268 ◽  
Author(s):  
M. van Oijen ◽  
C. Reyer ◽  
F.J. Bohn ◽  
D.R. Cameron ◽  
G. Deckmyn ◽  
...  
Keyword(s):  
Scots Pine ◽  
Bayesian Calibration ◽  
Forest Models ◽  
Pine Stands ◽  
Using Data ◽  
Scots Pine Stands

scholarly journals Forest floor temperature and greenness link significantly to canopy attributes in South Africa’s fragmented coastal forests

2018 ◽  
Author(s):  
Marion Pfeifer ◽  
Michael JW Boyle ◽  
Stuart Dunning ◽  
Pieter Olivier
Keyword(s):  
Land Use ◽  
Food Security ◽  
Surface Temperature ◽  
Habitat Quality ◽  
Ground Surface ◽  
Quality Metrics ◽  
Ground Vegetation ◽  
Ground Surface Temperature ◽  
Forest Models ◽  
Random Forest Models

Tropical landscapes are changing rapidly due to changes in land use and land management. Being able to predict and monitor land use change impacts on species for conservation or food security concerns requires the use of habitat quality metrics, that are consistent, can be mapped using above - ground sensor data and are relevant for species performance. Here, we focus on ground surface temperature (Thermalground) and ground vegetation greenness (NDVIdown) as potentially suitable metrics of habitat quality. We measure both across habitats differing in tree cover (natural grassland to forest edges to forests and tree plantations) in the human-modified coastal forested landscapes of Kwa-Zulua Natal, South Africa. We show that both habitat quality metrics decline linearly as a function of increasing canopy closure (FCover, %) and canopy leaf area index (LAI). Opening canopies by about 20% or reducing canopy leaf area by 1% would result in an increase of temperatures on the ground by more than 1°C, and an increase in ground vegetation greenness by 0.2 and 0.14 respectively. Upscaling LAI and FCover to develop maps from Landsat imagery using random forest models allowed us to map Thermalground and NDVIdown using the linear relationships. However, map accuracy was constrained by the predictive capacity of the random forest models predicting canopy attributes and the linear models linking canopy attributes to the habitat quality metrics. Accounting for micro-scale variation in temperature is seen as essential to improve biodiversity impact predictions. Our upscaling approach suggests that mapping ground surface temperature based on radiation and vegetation properties might be possible, and that canopy cover maps could provide a useful tool for mapping habitat quality metrics that matter to species. However, we need to increase sampling of surface temperature spatially and temporally to improve and validate upscaled models. We also need to link surface temperature maps to demographic traits of species of different threat status or functions in landscapes with different disturbance and management histories testing for generalities in relationships. The derived understanding could then be exploited for targeted landscape restoration that benefits biodiversity conservation and food security sustainably at the landscape scale.

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