Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals by Liuyi Ling, Zhu Lin, Bin Feng, Liyu Wei, Li Jin, Yiwen Wang

“…In the four motion mode experiments, the SSA-RF model achieved a minimum root-mean-square error of 1.569° for predicting knee joint angle, the average absolute error was only 1.05 °, and the coefficient of determination was as high as 0.99. …”
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Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree... by Idris Zubairu Sadiq, Fatima Sadiq Abubakar, Muhammad Auwal Saliu, Babangida Sanusi katsayal, Aliyu Salihu, Aliyu Muhammad

“…Results The results showed that random forest regression outperformed both boosting and decision tree regression, recording the lowest error criteria (MSE = 0.071 and RMSE = 0.266) for evaluating the model. …”
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Inversion and analysis of leaf area index (LAI) of urban park based on unmanned aerial vehicle (UAV) multispectral remote sensing and random forest (RF). by Yan Li, Bocheng Wang, Xuefei Zhao, Yichuan Zhang, Lifang Qiao

“…RF can effectively capture the complex nonlinear relationship between NDVI and LAI, with a coefficient of determination (R²) of 0.54 and a root mean square error (RMSE) of 0.91. Although the accuracy is still insufficient, RF's ability to handle nonlinear relationships makes it an effective tool for LAI inversion in complex vegetation environments. …”
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Perbandingan Kinerja Metode Arima, Multi-Layer Perceptron, dan Random Forest dalam Peramalan Harga Logam Mulia Berjangka yang Mengandung Pencilan by Teguh Prasetyo, Rizki Alifah Putri, Dini Ramadhani, Yenni Angraini, Khairil Anwar Notodiputro

“…Berdasarkan hal tersebut dalam artikel ini dibahas tentang hasil kajian perbandingan kinerja metode ARIMA, Multi-Layer Perceptron (MLP), dan Random Forest (RF) dalam peramalan data deret waktu yang mengandung pencilan, khususnya untuk data harga logam mulia berjangka (emas, perak, dan platina) berdasarkan nilai Mean Absolute Percentage Error (MAPE). …”
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Estimating Biomass in <i>Eucalyptus globulus</i> and <i>Pinus pinaster</i> Forests Using UAV-Based LiDAR in Central and Northern Portugal by Leilson Ferreira, André Salgado de Andrade Sandim, Dalila Araújo Lopes, Joaquim João Sousa, Domingos Manuel Mendes Lopes, Maria Emília Calvão Moreira Silva, Luís Pádua

“…For <i>P. pinaster</i>, only MLR was applied due to the limited number of field data, yet <i>R</i>² exceeded 0.80. Although absolute errors were higher for <i>Pinus pinaster</i> due to greater biomass variability, relative performance remained consistent across species. …”
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Estimation of Forest Canopy Cover by Combining ICESat-2&#x002F;ATLAS Data and Geostatistical Method&#x002F;Co-Kriging by Jinge Yu, Hongyan Lai, Li Xu, Shaolong Luo, Wenwu Zhou, Hanyue Song, Lei Xi, Qingtai Shu

“…Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. …”
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Field assessments on the impact of CO₂ concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests by D. Teng, D. Teng, J. Zhu, J. Zhu, J. Zhu, T. Gao, T. Gao, T. Gao, F. Yu, F. Yu, Y. Zhu, Y. Zhu, X. Zhou, X. Zhou, B. Yang

“…<span class="inline-formula"><i>A</i>_m</span> and <span class="inline-formula"><i>P</i>_m</span> are significantly correlated to the magnitude of and random error in <span class="inline-formula"><i>F</i>_s</span> with diurnal and seasonal differences. …”
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Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning by G. Burch Fisher, Andrew J. Elmore, Matthew C. Fitzpatrick, Darin J. McNeil, Jeff W. Atkins, Jeffery L. Larkin

“…Analysis of model results across both public and private lands in three highly forested conservation regions of Pennsylvania (the Poconos, PA Wilds, and Laurel Highlands) revealed a propensity for young overstory removals along forest edges, suggesting edge effects from inaccuracies in the underlying forest mask and mixed pixels contribute to errors of commission. …”
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Correction: Hanson, C.T. Cumulative Severity of Thinned and Unthinned Forests in a Large California Wildfire. <i>Land</i> 2022, <i>11</i>, 373 by Chad T. Hanson

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Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

“…Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. …”
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Structuring a textile knitting dataset for machine learning and data mining applicationsMendeley Data by Toufique Ahmed, Abu Saleh Muhammad Junayed

“…Among various machine learning models to predict GSM, Random Forest and XGBoost consistently outperformed across all metrics (R² score, Mean Absolute Error, and Mean Square Error).…”
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Low-cost phone-based LiDAR scanning technology provides sub-centimeter accuracy when measuring the main dimensions of motor-manual tree felling cuts by Stelian Alexandru Borz, Andrea Rosario Proto

“…The resulted point clouds were imported to Cloud Compare software, where the same measurements were taken digitally and used as data for comparison. By the commonly used error metrics such as the bias (−0.73–0.10), mean absolute error (0.51–0.78) and root mean squared error (0.68–0.92), the differences between the two were in the sub-centimeter domain. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

“…Among the models tested, the Random Forest algorithm demonstrated outstanding performance, achieving an R2 of 0.955, a Mean Squared Error (MSE) of 0.119, and an Average Absolute Relative Error (AARE%) of 7.683, highlighting its reliability and robustness in predicting ROP. …”
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Forest Three-Dimensional Reconstruction Method Based on High-Resolution Remote Sensing Image Using Tree Crown Segmentation and Individual Tree Parameter Extraction Model by Guangsen Ma, Gang Yang, Hao Lu, Xue Zhang

“…Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and forestry digital twins. …”
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Machine learning approach for multidimensional poverty estimation by Mario Esteban Ochoa Guaraca, Ricardo Castro, Alexander Arias Pallaroso, Antonia Machado, Dolores Sucozhañay

“…An error of 7.5% was obtained in the cross-validation and 7.48% with the test data set. …”
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Biochemical Oxygen Demand Prediction Based on Three-Dimensional Fluorescence Spectroscopy and Machine Learning by Xu Zhang, Yihao Zhang, Xuanyi Yang, Zhiyun Wang, Xianhua Liu

“…The BOD₅ values were effectively predicted by the random forest model with a high goodness of fit (R² = 0.878) and low mean square error (MSE = 0.28). …”
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Tree density has been underestimated in the mountainous regions of Northeast China by Yunkun Song, Wenqiang Xie, Fang Wu, Xuefeng Cui, Xiaodong Yan, Shuaifeng Song, Jun Ren, Hui Bai, Yu Zhang, Wei Pang, Yueying Xiao, Wang Zhan

“…Compared to global tree density datasets, our approach increased R2 to 0.454, while root mean square error (RMSE) and bias improved by 47.90 % and 74.52 %, respectively. …”
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Sentinel imagery detects the presence of live trees following large wildfires in California by Christopher Y S Wong, Micah C Wright, Phillip J van Mantgem, Andrew M Latimer, Derek J N Young

“…At the site level, the all bands model outperformed the vegetation index-based models (80%–85% vs 65%–79% accuracy). Errors were mainly false positives attributed to pixels with green understory vegetation but no live trees. …”
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Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

“…Faults such as string disconnections, module short-circuits, and shading effects have been identified using two key indicators: current error (Ec) and voltage error (Ev). By focusing on power losses as a fault indicator, this method provides high-accuracy fault detection without requiring extensive labeled data, a significant advantage for large-scale PV systems where data acquisition can be challenging. …”
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Predicción del diámetro sobre muñones en pinus taeda l. Origen marion, mediante curvas de perfil de fuste by Ernesto Andenmatten, Hugo Fassola, Féderico Letourneau, Paula Ferrere, Ernesto Crechi

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