Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments by Uzair Khan, Mohd Tariq, Arif I. Sarwat

“…The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. …”
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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

“…To improve the accuracy of LAI estimation in urban parks, this study, by combining unmanned aerial vehicle (UAV) multispectral remote sensing technology with Random Forest (RF) to conduct the inversion and analysis of LAI in Xinxiang People's Park. …”
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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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Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/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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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

“…Relative to differential-based VIs, our results highlight potential advantages of using post-fire Sentinel-2 imagery and random forest modeling for identifying live tree presence and scaling to full fire extents.…”
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Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis by M Darshan Teja, G Mokesh Rayalu

“…Model performance was assessed using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). …”
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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

“…Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. …”
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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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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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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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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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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

“…This approach can increase the accuracy of local tree density simulations, which is crucial for the precise modeling of the forest carbon sequestration potential, the development of targeted forest conservation strategies, and the implementation of effective carbon management practices.…”
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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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Visualización multi-escala de aciertos y errores de un mapa de usos de suelo: El caso de la cuenca del lago de Cuitzeo, Michoacán, México by Stephane Couturier, Valdemar Coria, Yannick Deniau, Francisco Javier Osorno Covarrubias

“…Presentamos aquí la visualización de aciertos y errores, con criterios de lógica difusa, del mapa de usos de suelo de alta taxonomía de la cuenca del lago de Cuitzeo extraído de la cartografía a escala 1:250,000 del Inventario Forestal Nacional del año 2000 en México. …”
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