LightGBM hybrid model based DEM correction for forested areas. by Qinghua Li, Dong Wang, Fengying Liu, Jiachen Yu, Zheng Jia

“…Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. …”
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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

“…The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. …”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

“…To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. …”
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Harmonizing remote sensing and ground data for forest aboveground biomass estimation by Ying Su, Zhifeng Wu, Xiaoman Zheng, Yue Qiu, Zhuo Ma, Yin Ren, Yanfeng Bai

“…Accurate aboveground biomass (AGB) estimation is crucial for evaluating management and conservation policy of forests. However, the complexity of forest ecosystems and the diversity of geography bring great challenges to traditional biomass estimation methods. …”
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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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Forest Fire Risk Assessment: An Illustrative Example from Ontario, Canada by W. John Braun, Bruce L. Jones, Jonathan S. W. Lee, Douglas G. Woolford, B. Mike Wotton

“…Burn-P3 simulations were run under the settings (related to weather) recommended in the software documentation and were found to be fairly robust to errors in the fuel map, but simulated fire sizes were substantially larger than those observed in the historic record. …”
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Predicting forest understory habitat for Canada lynx using LIDAR data by Patrick A. Fekety, Rema B. Sadak, Joel D. Sauder, Andrew T. Hudak, Michael J. Falkowski

“…Model fit statistics for normalized root mean square errors (RMSE%) were 30.8–33.7% and pseudo‐R2 ranged from 0.64 to 0.71. …”
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Conversion from Forest to Agriculture in the Brazilian Amazon from 1985 to 2021 by Hugo Tameirão Seixas, Hilton Luís Ferraz da Silveira, Alan Pereira da Silva Falcão Mendes, Fabiana Da Silva Soares, Ramon Felipe Bicudo da Silva

“…Our accuracy assessment shows an opportunity to improve conversion length calculations by reducing errors in the classification of agriculture establishment. …”
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Fuel detection in forest environments training deep learners with smartphone imagery by F. Pirotti, F. Pirotti, A. Carmelo, E. Kutchartt, E. Kutchartt, E. Kutchartt

“…It was observed that this was due mostly to omission errors due to low light conditions in the forestry environment. …”
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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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Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

“…The experimental results indicate that XGBoost achieved the best performance, with remarkably low error rates, producing a MAPE of 0.0022%, an RMSE of 0.0011, and a perfect R2 score of 1, thus being the most effective model for this application. …”
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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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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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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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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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Evaluation and improvement of the vertical accuracy of the global open DEM under forest environment by Jiapeng Huang, Xiaozhu Yang

“…The research findings indicate that the accuracy of GEDI02_A is the highest, with Root Mean Square Error (RMSE)=6.20m. Next, the Federal Railway Authority of Germany’s DEM (FABDEM) with RMSE = 8.46 m. …”
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