Bayesian weighted random forest for classification of high-dimensional genomics data by Oyebayo Ridwan Olaniran, Mohd Asrul A. Abdullah

“…The new model Bayesian Weighted Random Classification Forest (BWRCF) arises from the modification of the existing random classification forest in two ways. …”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…The model used in this study is Random Forest (RF), which belongs to the family of ensemble learning models.The data used in this study come from numerical simulations carried out with Matlab and consist of 1400 samples, derived from the analysis of 35 thermal insulators distributed across 20 climate zones. …”
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The Difference in MODIS Aerosol Retrieval Accuracy over Chinese Forested Regions by Masroor Ahmed, Yongjing Ma, Lingbin Kong, Yulong Tan, Jinyuan Xin

“…Nevertheless, the long-term accuracy of this data remains under-evaluated, especially for the forested areas. This study was undertaken to substantiate the accuracy of MODIS Terra (MOD04) and Aqua (MYD04) at 3 km resolution AOD retrievals at six forested sites in China from 2004 to 2022. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. …”
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A Mean Weighted Squared Error-based Neural Classifier for Intelligent Pattern Recognition in Smart Grids by Mehdi Khashei, Mehrnaz Ahmadi, Fatemeh Chahkoutahi

“…This paper introduces a new extension of the conventional Mean Squared Error loss function, called Mean Weighted Squared Error (MWSE), specifically designed for renewable energy classification purposes. …”
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Analysis and reduction of topographic effect induced errors in land surface temperature retrieval over the Tibetan Plateau by Yuejie Zhang, Qinghong Sheng, Kerui Li, Bo Wang, Jun Li, Xiao Ling, Fan Gao

“…Random forest (RF) models were employed to assess the contribution of topographic effects to these errors. …”
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Improving Biomass Estimation in Ethiopian Moist Afromontane Forest Through Volume Model by Mulatu Abu, Negash Mesele, Tolera Motuma

“…The study demonstrated that species-specific volume models reduce errors in the estimation of forest volume and biomass.…”
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Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida by Inacio T. Bueno, Carlos A. Silva, Caio Hamamura, Victoria M. Donovan, Ajay Sharma, Jiangxiao Qiu, Jinyi Xia, Kody M. Brock, Monique B. Schlickmann, Jeff W. Atkins, Denis R. Valle, Jason Vogel, Andres Susaeta, Mauro A. Karasinski, Carine Klauberg

“…Abstract Hurricane Ian caused aboveground biomass density (AGBD) losses across Florida’s forests in the United States, highlighting the need for accurate, large-scale monitoring tools. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. …”
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Global Elevation Inversion for Multiband Spaceborne Lidar: Predevelopment of Forest Canopy Height by Haowei Zhang, Wei Gong, Hu He, Yue Ma, Weibiao Chen, Jiqiao Liu, Ge Han, Zhiyu Gao, Wanqi Zhong, Xin Ma

“…Accurate forest canopy heights can be obtained using the decomposed signal approach in MBFA, which has been verified in Finland. …”
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A novel analysis of random forest regression model for wind speed forecasting by Sathyaraj J, Sankardoss V

“…This article uses a random forest regression (RFR) model to analyze wind speed forecasting. …”
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Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands by Jason Beaver, Elyn R. Humphreys, Douglas King

“…Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. For annual GPP, MODIS (R2 = 0.84; mean absolute error 40.5 g C m−2  year−1) also outperformed the HLS models (R2 = 0.46; mean absolute error 86.4 g C m−2  year−1).…”
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Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest by Jiang Yuan, Huailei Cheng, Lijun Sun, Yadong Cao, Ruikang Yang, Tian Jin, Mingchen Li

“…Further validation revealed that the determination coefficient exceeded 0.94 and the mean absolute error remained below 2.3 °C at all depths. In summary, the transfer learning approach based on the random forest model demonstrates strong adaptability to different regions. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…A comparison between the proposed prediction model and several mainstream machine-learning algorithms indicates that the Random Forest model performs superiorly in both the coefficient of determination (R²) and the mean squared error (MSE). …”
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A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning by Xuan Sun, Ning Li, Duoqi Chen, Guang Chen, Changjun Sun, Mulin Shi, Xuehong Gao, Kuo Wang, Ibrahim M. Hezam

“…Results from the validation process reveal that during the natural development period of the “3.29 Forest Fire,” the FFSPP model predicts a burned area of 286.81 hm², with an associated relative error of 28.94%. …”
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How many plots are needed to estimate sapling density and stocking in temperate forests? by François Guillemette, Marie-Claude Lambert, Hugues Power, Patricia Raymond

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Evaluation and improvement of the vertical accuracy of the global open DEM under forest environment by Jiapeng Huang, Xiaozhu Yang

“…Canopy height exhibits a higher correlation with the estimated accuracy of forest understory terrain. Finally, optimizing FABDEM based on the mathematical interpolation method using GEDI02_A reduces the RMSE from 8.46 m to 6.83 m. …”
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Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic by Efraín Duarte, Erick Zagal, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández

“…In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. …”
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A Comparative Analysis of Burned Area Datasets in Canadian Boreal Forest in 2000 by Laia Núñez-Casillas, José Rafael García Lázaro, José Andrés Moreno-Ruiz, Manuel Arbelo

“…The turn of the new millennium was accompanied by a particularly diverse group of burned area datasets from different sensors in the Canadian boreal forests, brought together in a year of low global fire activity. …”
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Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing by Thomas Leditznig, Hermann Klug

“…Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. …”
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