Assimilating satellite‐based canopy height within an ecosystem model to estimate aboveground forest biomass by E. Joetzjer, M. Pillet, P. Ciais, N. Barbier, J. Chave, M. Schlund, F. Maignan, J. Barichivich, S. Luyssaert, B. Hérault, F. vonPoncet, B. Poulter

“…While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE‐CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.…”
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Performance evaluation and improvement of ICESat-2 and GEDI forest canopy height retrievals in Northeast China by Cancan Yang, Daoli Peng, Nan Zhang, Mingjie Chen, Weisheng Zeng, Xiangnan Sun, Longwei Li, Weitao Li

“…The advent of new-generation spaceborne Light Detection and Ranging (lidar) systems, exemplified by the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), has opened up an unprecedented opportunity for observing forest canopy structures. However, forest canopy height derived from ICESat-2 ATL08 land and vegetation products and GEDI L2A geolocated elevation and height products exhibit varying accuracy across different regions. …”
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Speed Prediction of Urban Rail Transit Trains Based on Random Forest & Neural Network by QIN Jiannan, HU Wenbin, XU Li

“…The results of model testing on the simulation data and actual line data show that the proposed algorithm can effectively predict the speed curve of the train in real time, improve the accuracy of speed tracking control. The error is reduced by 57.7% compared with the traditional neural network model, and the error is reduced by 73.9% compared with the random forest regression model.…”
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An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics by Shuo Zhang, Mengya Pan

“…Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…Mean squared error (MSE) and root mean square error (RMSE) were employed to assess the primary performance indicators of the models, while mean absolute error (MAE) and the R-squared value were used to evaluate the goodness of fit of the models.Results: The RMSE, MAE and MSE obtained for the Spark-based implementation were lower, compared to the MapReduce-based implementation, although these low values indicate high prediction accuracy. …”
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Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software by Tucumbi Lisbeth, Guano Jefferson, Salazar-Achig Roberto, Jiménez J. Diego L.

“…For this purpose, open-source software (Python) and a methodology involving the creation, implementation, and testing of specific machine learning models random forest (RF) and decision tree (DT) were used. The metrics used to identify the effectiveness of the models in predicting solar radiation were the coefficient (R2), the mean square error (MSE), and the mean absolute error (MAE). …”
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Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm by Osvaldo Pérez, Brian Diers, Nicolas Martin

“…Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season.…”
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Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and Chair‐Stand Test by Giulia Giordano, Luca Mastrantoni, Francesco Landi, The Lookup 8+ Study Group

“…After a 70/20/10 split in training, validation and test set, a quantile regression forest (QRF) was trained. Performance metrics were R‐squared (R2), mean squared error (MSE), root mean squared error (RMSE) and mean Winkler interval score (MWIS) with 90% prediction coverage (PC). …”
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Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…The obtained results demonstrated that the DBO algorithm significantly could enhance the Random Forest (RF) model's predictive accuracy, the Mean Absolute Error (MAE) and the Mean Square Error (MSE) were reduced to 0.30321 and 0.16382 respectively, the coefficient of determination (R²) had increased to 0.86255. …”
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How to implement the data collection of leaf area index by means of citizen science and forest gamification? by Shaohui Zhang, Lauri Korhonen, Timo Nummenmaa, Simone Bianchi, Matti Maltamo

“…However, more images may be needed in forests with large LAI or uneven canopy structure to avoid large errors. …”
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Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength by Hayder Riyadh Mohammed Mohammed, Sumarni Ismail

“…In the quantitative term, the minimal root mean square error value was attained (RMSE = 89.68 kN).…”
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Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains by Jordan Golinkoff, Mauricio Zapata-Cuartas, Emily Witt, Adam Bausch, Donal O’Leary, Reza Khatami, Wu Ma

“…The application of this method relies on user-defined levels of risk and inventory confidence combined with the distribution of observed error. This method allows remote sensing estimates of carbon stocks to be applied to forest carbon offset quantification. …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…While Random Forest consistently delivered high R² values and low root mean square errors (RMSE) across all attributes, GBTA showed particular strength in predicting standing stock, and CART excelled in basal area estimation but was less reliable for other attributes. …”
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Explainable forecasting of air quality index using a hybrid random forest and ARIMA model by Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, Tabassum Ara

“…Compared to baseline models, the hybrid approach achieves lower Mean Squared Error (MSE = 508.46) and a higher R² score (0.94), confirming improved generalization. …”
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Learning deep forest for face anti-spoofing: An alternative to the neural network against adversarial attacks by Rizhao Cai, Liepiao Zhang, Changsheng Chen, Yongjian Hu, Alex Kot

“…Our experiments show that the CNN or ViT models could have at least an 8% equal error rate (EER) increment when encountering adversarial examples. …”
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InSAR-based estimation of forest above-ground biomass using phase histogram technique by Chuanjun Wu, Peng Shen, Stefano Tebaldini, Mingsheng Liao, Lu Zhang

“…This paper introduces a method for estimating forest above-ground biomass (AGB) using the Interferometric SAR (InSAR)-based Phase Histogram (PH) technique. …”
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Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“…From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. …”
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Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“…From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. …”
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IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH by Sumin Sumin, Prihantono Prihantono, Khairawati Khairawati

“…This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correct errors in Single Tuition Fee (STF) classification. …”
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XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation by Robin K.E. Tau, Abid Yahya, Mmoloki Mangwala, Nonofo M.J. Ditshego

“…HEAD-KF yields a global mean-absolute error of Image 5 SOC, keeps dynamic-discharge error to Image 6, and updates in Image 7 while consuming Image 8 per prediction. …”
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