Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…Regression models for the yield stress and its first-order derivatives based on the Yld2000–2d yield criterion are developed using several machine learning algorithms, including Random Forest (RF), Multilayer Perceptron (MLP), Histogram-Based Gradient Boosting (HGB), and Support Vector Machine (SVM). …”
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Estimation of Chlorophyll Content at Stand and Individual Tree Level by UAV Hyperspectral Combined with LiDAR by Zhuonan Meng, Ying Yu, Xiguang Yang, Tao Yang

“…The accuracy of the models was evaluated by the coefficient of determination (R²) and root mean square error (RMSE). The results show that random forest (R² = 0.59–0.64, RMSE = 3.79–5.83 µg·cm⁻²) among all statistical models is superior to other models. …”
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A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation by Shalini Mahanthege, William Kleiber, Karl Rittger, Balaji Rajagopalan, Mary J. Brodzik, Edward Bair

“…., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%.…”
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Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context by Marcelo Bueno, Carlos Baca García, Nilton Montoya, Pedro Rau, Hildo Loayza

“…Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. …”
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Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. Comparative analysis unequivocally demonstrates the superior predictive performance of the random forest model, achieving a remarkable R-squared value of 97.4 % and a root mean square error of 165. …”
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Estimation of Tree Diameter at Breast Height (DBH) and Biomass from Allometric Models Using LiDAR Data: A Case of the Lake Broadwater Forest in Southeast Queensland, Australia by Zibonele Mhlaba Bhebhe, Xiaoye Liu, Zhenyu Zhang, Dev Raj Paudyal

“…Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R<sup data-eusoft-scrollable-element="1">2</sup>). …”
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Impact of Parameters and Tree Stand Features on Accuracy of Watershed-Based Individual Tree Crown Detection Method Using ALS Data in Coniferous Forests from North-Eastern Poland by Marcin Kozniewski, Łukasz Kolendo, Szymon Chmur, Marek Ksepko

“…The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. …”
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<i>In situ</i> and <i>ex situ</i> variability of phenological and morphological features in <i>Caltha palustris</i> L. under the conditions of the West Siberian forest steppe by Т. I. Fomina

“…The goal was to assess the variance in phenological and morphological features of C. palustris in the forest steppe of Western Siberia.Materials and methods. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…For the studied crops, all models had mean absolute errors and root mean squared errors not exceeding 6 dt/ha, which is relatively small because it is under 20% of the mean yield. …”
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Choosing blocks for spatial cross-validation: lessons from a marine remote sensing case study by Andy Stock, Andy Stock

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Data Mining Techniques in Decision Making by Amna Sajid, Basit Amin

“…Decision Tree, Naive Bayes, SMO, bagging, and Random Forest were chosen for precision, recall, and F-measure. …”
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Performance evaluation of machine learning techniques for heart disease prediction: An overview by Dhanashri Shankar Karande, Shailendrakumar Mahadeo Mukane

“…ML techniques, including Random Forest, ANN, Linear Regression (LR), Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting, and Decision Tree (DT), are utilized to create the machine learning model. …”
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Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction by Geng-Fu He, Pin Zhang, Zhen-Yu Yin

“…In the regions of sparse data, predicted uncertainty becomes larger as errors increase, demonstrating the validity of UQ. …”
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Interpretation of digital imagery to estimate juvenile stand attributes in managed boreal stands, density, stocking and height by Douglas E.B. Reid, Jevon Hagens

“…Forest regeneration monitoring is critical to inform forest management planning, evaluate silvicultural efficacy, and determine achievement of renewal standards in managed forests. …”
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Multi-Band Scattering Characteristics of Miniature Masson Pine Canopy Based on Microwave Anechoic Chamber Measurement by Kai Du, Yuan Li, Huaguo Huang, Xufeng Mao, Xiulai Xiao, Zhiqu Liu

“…In addition, applying orientation correction to the polarization scattering matrix can mitigate the impact of the incident angle and reduce the decomposition energy errors in the Freeman–Durden model. In order to ensure the reliability of forest parameter inversion based on SAR data, a greater emphasis should be placed on physical models that account for signal scattering and the extinction process, rather than relying on empirical models.…”
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Nanocellulose Addition to Paper and the ‘Cai Lun Principle’ – Maybe Not Such a Good Idea After All by Martin A. Hubbe

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Machine learning-based stem taper model: a case study with Brutian pine by Fadime Sağlam

“…This research highlights the potential of ML techniques in forest modeling, offering enhanced accuracy and efficiency for forest inventory and management applications.…”
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PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE by Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry

“…Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). …”
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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). Among the machine learning models, random forest regressor yielded high R2 value, low mean squared error (MSE), and mean absolute error (MAE). …”
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Explainable AI analysis for smog rating prediction by Yazeed Yasin Ghadi, Sheikh Muhammad Saqib, Tehseen Mazhar, Ahmad Almogren, Wajahat Waheed, Ayman Altameem, Habib Hamam

“…Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R2) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. …”
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