Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. …”
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Spatial-temporal distribution patterns change of grassland formation in Inner Mongolia since the 1980s by Anan Zhang, Jiakui Tang, Na Zhang, Xuefeng Xu, Wuhua Wang, Xiaofan Li, Maojin Li, Kaihui Li, Mengquan Wu, Shuohao Cai

“…This study evaluates the degradation of Inner Mongolian grasslands from 1980 to 2020 by analysing changes in grassland formations. A random forest-based machine learning classification model was developed using the Vegetation Map of China (1:1,000,000, 1980 s), the Vegetation Map of Inner Mongolia (1:250,000, 2009), and multi-source datasets. …”
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An Upscaling-Based Strategy to Improve the Ephemeral Gully Mapping Accuracy by Solmaz Fathololoumi, Daniel D. Saurette, Harnoordeep Singh Mann, Naoya Kadota, Hiteshkumar B. Vasava, Mojtaba Naeimi, Prasad Daggupati, Asim Biswas

“…The errors for EGs maps at various resolutions revealed an increase in identification error with higher spatial resolution. …”
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Unveiling the performance and influential factors of GEDI L2A for building height retrieval by Peimin Chen, Huabing Huang, Peng Qin, Zhenbang Wu, Zixuan Wang, Chong Liu, Na Dong, Jie Wang

“…While the Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) was primarily designed for forest measurements, it also holds potential for large-scale building height retrieval. …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…Finally, the CEEMDAN-RF hybrid model is best model based on the lowest observed errors of Root mean square error (RMSE): 0.13, Mean square error (MSE): 0.02 and high R2: 0.94, hence this model is appropriate for prediction of river water level. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…The performance was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Garbage prediction using regression analysis for municipal corporations of Indian cities by Raj Kumar Sharma, Manisha Jailia

“…Random Forest Regression (RFR) with (MSE: 100,078.749 & MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Coefficient of determination (R2), mean absolute error (MAE), and mean relative error (MRE) were used as error indicators to evaluate the potential of the six machine learning algorithms for predicting NDVI changes. …”
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Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation by Rong Mei, ChengJiang Yu

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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…Performance evaluation was conducted on the linear regression, random forest and artificial neural networks (ANN) through statistical metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). …”
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Forecasting the Remaining Duration of an Ongoing Solar Flare by Jeffrey W. Reep, Will T. Barnes

“…This random forest model is computationally light enough to be performed in real time, allowing for the prediction to be made during the course of a flare.…”
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Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group by Wei Jia

“…The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.…”
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A Method of the Vibration Information Detection for Rotating Machinery Based on the Rolling-Shutter CMOS and Digital Image Processing by Yonggong Yuan, Ji Zhang, Nanwangdi Li, Haifeng Wang, Yuxin Hu, Yilong Wang, Ning Mei, Han Yuan

“…Comparative analysis of the diagnostic results using the K-Nearest Neighbor, AdaBoost, CatBoost, and Random Forest algorithms revealed that the Random Forest algorithm achieved the highest diagnostic accuracy, exceeding 98%. …”
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Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…In conclusion, the SAHI-Improved-YOLOv8 has the capability of efficiently processing high-resolution images, which alleviates the problems of high density of small targets, false detections, missed detections, and high localization error. In practical applications, the SAHI-Improved-YOLOv8 model performs excellently in tree pit detection in UAV imagery, significantly reducing false detections and missed detections, and providing reliable technology support for large-scale forest management.…”
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Estimation of Above-Ground Biomass for <italic>Dendrocalamus Giganteus</italic> Utilizing Spaceborne LiDAR GEDI Data by Huanfen Yang, Zhen Qin, Qingtai Shu, Li Xu, Jinge Yu, Shaolong Luo, Zaikun Wu, Cuifen Xia, Zhengdao Yang

“…The outcomes reveal that 1) the results showed that the power function emerged as the most efficacious model, with coefficient of determination (<italic>R</italic>²) &#x003D; 0.87 and root mean square error (RMSE) &#x003D; 0.00051 Mg, in estimating the AGB of <italic>Dendrocalamus giganteus</italic>. 2) Based on the feature importance ranking of Random Forest, five variables were selected from the 40 extracted from GEDI, achieving RMSE &#x003D; 8.21 Mg&#x002F;ha and mean absolute error (MAE) &#x003D; 6.12 Mg&#x002F;ha. …”
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Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture by Kamel Lahssini, Nicolas Baghdadi, Guerric le Maire, Guerric le Maire, Ibrahim Fayad, Ibrahim Fayad, Ludovic Villard

“…Canopy height is a key indicator of tropical forest structure. In this study, we present a deep learning application to map canopy height in French Guiana using freely available multi-source satellite data (optical and radar) and complementary environmental information. …”
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ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized by Vinicius Lins Costa Ok Melo, Pedro Emmanuel Alvarenga Americano do Brasil, PhD

“…A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression. …”
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Epidemiological Manifestation of Combined Natural Foci of Tularemia, Leptospirosis and Hemorrhagic Fever with Renal Syndrome: Mixed Infections by T. N. Demidova, N. E. Sharapova, V. V. Gorshenko, T. V. Mikhailova, A. S. Semihin, A. E. Ivanova

“…The analysis of our own research and literature data allowed us to characterize the combined foci of tularemia, leptospirosis and HFRS as bacterial-viral, according to the degree of combination in the parasitic system of common reservoir hosts, such as common, red, water voles, forest, field and house mice, insectivores. According to the level of combination of the morphological structure of the landscape, the foci belong to steppe, meadow-field, forest and floodplain-swamp, and by type these foci are characterized as infectious geographically combined. …”
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A framework for modelling whole-lung and regional transfer factor of the lung for carbon monoxide using hyperpolarised xenon-129 lung magnetic resonance imaging by Jemima H. Pilgrim-Morris, Laurie J. Smith, Helen Marshall, Bilal A. Tahir, Guilhem J. Collier, Neil J. Stewart, Jim M. Wild

“…The random forest model was applied voxel-wise to 129Xe images to yield regional TL maps. …”
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Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network by Min Seop So, Mohammad Mahruf Mahdi, Duck Bong Kim, Jong-Ho Shin

“…The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. …”
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