Mapping forest aboveground carbon stock of combined stratified sampling and RFRK model with mean annual temperature and precipitation by Min Peng, Mingrui Xu, Jialong Zhang, Bo Qiu, Chenkai Teng, Chaoqing Chen

“…Abstract Accurately estimating forest aboveground carbon stock (ACS) is essential for achieving carbon neutrality. …”
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Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data. by Ali Golestani, Nazila Rezaei, Mohammad-Reza Malekpour, Naser Ahmadi, Seyed Mohammad-Navid Ataei, Sepehr Khosravi, Ayyoob Jafari, Saeid Shahraz, Farshad Farzadfar

“…Merging this data with a weather-related dataset resulted in a comprehensive dataset containing location, time, weather, and error type variables. After preprocessing, 619,988 records without any missing values were used to train and compare the performance of six machine learning models including logistic regression, K-nearest neighbors, random forest, Extreme Gradient Boosting (XGBoost), Naïve Bayes, and support vector machine. …”
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Modelling temporal change in inventory attributes from a LiDAR-derived inventory for the United Counties of Prescott and Russell, Ontario: A comparison of random forest and linear... by Benjamin Gwilliam

“…As well, root mean square error was lower using random forest as opposed to linear regression for all three attributes, suggesting random forest produced more accurate results overall. …”
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Accuracy of Vegetation Height and Terrain Elevation Derived from Terrestrial Ecosystem Carbon Inventory Satellite in Forested Areas by Zhao Chen, Sijie He, Anmin Fu

“…Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. …”
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Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. …”
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Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty by Durga Joshi, Chandi Witharana

“…Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. …”
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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio by Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka

“…The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. …”
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Accurate prediction of college students' information anxiety based on optimized random forest and category boosting fusion model by Bin Wang, Li Shao

“…The results showed that the model integrating random forest and category boosting algorithm had the lowest mean absolute error and root mean squared error, which were 0.125 and 0.142. …”
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Forecasting basal area increment in forest ecosystems using deep learning: A multi-species analysis in the Himalayas by P. Casas-Gómez, J.F. Torres, J.C. Linares, A. Troncoso, F. Martínez-Álvarez

“…This study addresses the task of forecasting Basal Area Increment trends in forest ecosystems, which is essential for conservation and biodiversity management, particularly in the context of climate change. …”
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Predicting Aboveground Carbon Storage in Different Types of Forests in South Subtropical Regions Using Machine Learning Models by Jiarun Liu, Zihang Yang, Lin Li, Xiaoxue Chu, Shiguang Wei, Juyu Lian

“…ABSTRACT Motivated by the need to enhance the accuracy of forest aboveground carbon storage (ACS) assessments, this study aimed to explore the effectiveness of different machine learning models in predicting ACS across various subtropical forest types in southern China. …”
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Incorporating stand parameters in nonlinear height-diameter mixed-effects model for uneven-aged Larix gmelinii forests by Muhammad Junaid Ismail, Tika Ram Poudel, Akber Ali, Lingbo Dong

“…Tree attributes, such as height (H) and diameter at breast height (D), are essential for predicting forest growth, evaluating stand characteristics and developing yield models for sustainable forest management. …”
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Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations by Gonzalo Gavilán-Acuna, Nicholas C. Coops, Piotr Tompalski, Pablo Mena-Quijada, Andrés Varhola, Dominik Roeser, Guillermo F. Olmedo

“…While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. …”
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China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022 by R. Shang, R. Shang, X. Lin, J. M. Chen, J. M. Chen, Y. Liang, K. Fang, M. Xu, Y. Yan, W. Ju, G. Yu, N. He, L. Xu, L. Liu, J. Li, W. Li, J. Zhai, Z. Hu

“…Additionally, adjustments were made for underestimations in the Northeastern and Southwestern regions of China identified in CAFA V1.0 using additional reference age samples and region-specific and forest-type-specific models. Validation, using a randomly selected 30 % of two reference datasets, indicated that the mapped age of disturbed forest exhibited a small error of <span class="inline-formula">±2.48</span> years, while the mapped age of undisturbed forest from 1986 to 2022 had a larger error of <span class="inline-formula">±7.91</span> years. …”
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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…The research is carried out using two machine learning algorithms: Random Forest and gradient boosting. The process includes analyzing important metrics, visualizing solutions, evaluating the performance of each model, and analyzing error matrices for attack categories. …”
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The Sloping Mire Soil-Landscape of Southern Ecuador: Influence of Predictor Resolution and Model Tuning on Random Forest Predictions by Mareike Ließ, Martin Hitziger, Bernd Huwe

“…The recursive partitioning algorithm Random Forest was used to predict the spatial water stagnation pattern and the thickness of the organic layer from terrain attributes. …”
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Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model by Sivaraja M., Swaminathen A. N., Kuttimarks M. S., Rajprasad J., Sakthivel M., Rex J.

“…In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. …”
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Hybrid Harris hawks-optimized random forest model for detecting multi-element geochemical anomalies related to mineralization by Hamid Sabbaghi, Nader Fathianpour

“…This research demonstrates that Harris hawks optimized random forest (HHORF) model is a vigorous procedure to identify multi-element geochemical anomalies. …”
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CONDITIONS OF HARVEST FORMATION AND EFFICIENCY OF OATS VARIETY ‘KONKUR’ CULTIVATION DEPENDING ON AGRO TECHNOLOGIES IN FOREST STEPPE OF POVOLZHIE by V. G. Vlasov, L. G. Zakharova

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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…The analysis reveals that XGBoost delivers the best performance, with a Mean Absolute Error (MAE) of 6,665 and an R² score of 0.978, outperforming Random Forest (MAE: 9,209; R²: 0.947) and Linear Regression (MAE: 10,917; R²: 0.896). …”
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Ultra-Wideband-Based Method for Measuring Tree Positions With Decimeter-Level Accuracy Under a Forest Canopy by Zuoya Liu, Harri Kaartinen, Teemu Hakala, Heikki Hyyti, Juha Hyyppa, Antero Kukko, Ruizhi Chen, Mikko Vastaranta

“…To evaluate the accuracy of the developed method, field tests were conducted in a boreal forest zone in Evo, Finland. Experimental results show that the developed method measured tree positions with a root-mean-square-error of 0.13 m compared to the total station-based reference and 0.14 m compared to the high-density airborne laser scanning-based reference measurements. …”
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