USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

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Identification of a potential fibromyalgia diagnosis using random forest modeling applied to electronic medical records

Journal of Pain Research ◽

10.2147/jpr.s82566 ◽

2015 ◽

pp. 277 ◽

Cited By ~ 1

Author(s):

Elizabeth T Masters ◽

Birol Emir ◽

Jack Mardekian ◽

Andrew Clair ◽

Max Kuhn ◽

...

Keyword(s):

Random Forest ◽

Electronic Medical Records ◽

Medical Records ◽

Forest Modeling

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Assessing the Carbon Storage of Soil and Litter from National Forest Inventory Data in South Korea

Forests ◽

10.3390/f11121318 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1318

Author(s):

Sunjeoung Lee ◽

Seunghyun Lee ◽

Joonghoon Shin ◽

Jongsu Yim ◽

Jinteak Kang

Keyword(s):

Random Forest ◽

Soil Carbon ◽

Forest Inventory ◽

National Forest Inventory ◽

Carbon Stocks ◽

National Forest ◽

Litter Layer ◽

Forest Models ◽

Random Forest Models ◽

Soil Carbon Stocks

Research Highlights: The estimation of soil and litter carbon stocks by the Land Use, Land-Use Changes, and Forestry (LULUCF) sectors has the potential to improve reports on national greenhouse gas (GHG) inventories. Background and Objectives: Forests are carbon sinks in the LULUCF sectors and therefore can be a comparatively cost-effective means and method of GHG mitigation. Materials and Methods: This study was conducted to assess soil at 0–30 cm and litter carbon stocks using the National Forest Inventory (NFI) data and random forest (RF) models, mapping their carbon stocks. The three main types of forest in South Kora were studied, namely, coniferous, deciduous, and mixed. Results: The litter carbon stocks (t C ha−1) were 4.63 ± 0.18 for coniferous, 3.98 ± 0.15 for mixed, and 3.28 ± 0.13 for deciduous. The soil carbon stocks (t C ha−1) were 44.11 ± 1.54 for deciduous, 35.75 ± 1.60 for mixed, and 33.96 ± 1.62 for coniferous. Coniferous forests had higher litter carbon stocks while deciduous forests contained higher soil carbon stocks. The carbon storage in the soil and litter layer increased as the forest grew older; however, a significant difference was found in several age classes. For mapping the soil and litter carbon stocks, we used four random forest models, namely RF1 to RF4, and the best performing model was RF2 (root mean square error (RMSE) (t C ha−1) = 1.67 in soil carbon stocks, 1.49 in soil and litter carbon stocks). Our study indicated that elevation, accessibility class, slope, diameter at breast height, height, and growing stock are important predictors of carbon stock. Soil and litter carbon stock maps were produced using the RF2 models. Almost all prediction values were appropriated to soil and litter carbon stocks. Conclusions: Estimating and mapping the carbon stocks in the soil and litter layer using the NFI data and random forest models could be used in future national GHG inventory reports. Additionally, the data and models can estimate all carbon pools to achieve an accurate and complete national GHG inventory report.

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Random forest modeling for the kinetic and isotherm study of malachite green adsorption from aqueous environments using zinc sulfide nanoparticle loaded with activated carbon

10.5004/dwt.2017.21393 ◽

2017 ◽

pp. 258-273 ◽

Cited By ~ 1

Author(s):

A. Ansari ◽

M. Ghaedi ◽

A.M. Ghaedi ◽

F. Bahari ◽

G. Azarian ◽

...

Keyword(s):

Activated Carbon ◽

Random Forest ◽

Zinc Sulfide ◽

Malachite Green ◽

Isotherm Study ◽

Aqueous Environments ◽

Forest Modeling

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Random Forest Modeling for Network Intrusion Detection System

Procedia Computer Science ◽

10.1016/j.procs.2016.06.047 ◽

2016 ◽

Vol 89 ◽

pp. 213-217 ◽

Cited By ~ 99

Author(s):

Nabila Farnaaz ◽

M.A. Jabbar

Keyword(s):

Random Forest ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Network Intrusion Detection System ◽

Forest Modeling

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Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling

Scientific Reports ◽

10.1038/srep28665 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

Jason Deglint ◽

Farnoud Kazemzadeh ◽

Daniel Cho ◽

David A. Clausi ◽

Alexander Wong

Keyword(s):

Random Forest ◽

Color Image ◽

Image Sensor ◽

Forest Modeling ◽

Non Linear

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Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study (Preprint)

10.2196/preprints.27977 ◽

2021 ◽

Author(s):

Kristin Nicole Gmunder ◽

Jose W Ruiz ◽

Dido Franceschi ◽

Maritza M Suarez

Keyword(s):

Health Care ◽

Logistic Regression ◽

Random Forest ◽

Health System ◽

Text Message ◽

Mixed Effects ◽

Descriptive Statistics ◽

Phone Call ◽

Patient Portal ◽

Forest Modeling

BACKGROUND With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. OBJECTIVE The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. METHODS Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. RESULTS Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. CONCLUSIONS The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

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