Validation of Global Moderate-Resolution FAPAR Products over Boreal Forests in North America Using Harmonized Landsat and Sentinel-2 Data by Yinghui Zhang, Hongliang Fang, Zhongwen Hu, Yao Wang, Sijia Li, Guofeng Wu

“…On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. …”
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Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions by Youssef Hamou-Ali, Nourlhouda Karmouda, Ismail Mohsine, Tarik Bouramtane, Ilias Kacimi, Sarah Tweed, Sarah Tweed, Mounia Tahiri, Nadia Kassou, Ali El Bilali, Omar Chafki, Mohamed Abdellah Ezzaouini, Siham Laraichi, Abdelaaziz Zerouali, Marc Leblanc, Marc Leblanc, Marc Leblanc

“…To address this limitation, a Random Forest-based model was employed to downscale GRACE TWS data from 100 km to 1 km resolution over Morocco, a drought-prone region, covering the period from 2002 to 2022. …”
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Resource Characteristics of Common Reed (<i>Phragmites australis</i>) in the Syr Darya Delta, Kazakhstan, by Means of Remote Sensing and Random Forest by Azim Baibagyssov, Anja Magiera, Niels Thevs, Rainer Waldhardt

“…Out of the 48 RF models developed, those utilizing the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) as key predictors yielded the best standing reed biomass estimation results, achieving a predictive accuracy of R² = 0.93, Root Mean Square Error (RMSE) = 2.74 t ha⁻¹ during the calibration, and R² = 0.83, RMSE = 3.71 t ha⁻¹ in the validation, respectively. …”
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Modelling decadal trends and the impact of extreme events on carbon fluxes in a temperate deciduous forest using a terrestrial biosphere model by T. Thum, T. Miinalainen, T. Miinalainen, O. Seppälä, H. Croft, C. Rogers, R. Staebler, S. Caldararu, S. Zaehle

“…In this work, we use in situ measurements of leaf chlorophyll content (Chl<span class="inline-formula">_Leaf</span>, 2013–2016) and the leaf area index (LAI, 1998–2018) to parameterize the seasonal dynamics of the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) terrestrial biosphere model (TBM) to simulate the carbon fluxes at the Borden Forest Research Station flux tower site, Ontario, Canada, over 22 years from 1996 to 2018. …”
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Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

“…One of the machine learning methods that can be used to predict GDM is the Support Vector Machine (SVM) algorithm and the Random Forest (RF) algorithm. This study aims to compare, and evaluate GDM disease prediction models using the SVM and RF algorithms by balancing the target data using the Random Undersampling Technique. …”
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Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah by Evita Fitri

“…The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. …”
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Random Forest-Based Machine Learning Model Design for 21,700/5 Ah Lithium Cell Health Prediction Using Experimental Data by Sid-Ali Amamra

“…The developed models were evaluated using two performance metrics, including R² and root mean squared error (RMSE). The obtained results show that the random forest model outperformed the SVR model, achieving an R² of 0.92 and an RMSE of 0.06, compared to an R² of 0.85 and an RMSE of 0.08 for SVR. …”
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A New Change Point Estimation of Forest Photosynthetic Phenology Method Based on the Maximum Perpendicular Distance Using Solar-Induced Chlorophyll Fluorescence by Chaoya Dang, Qingwei Zhuang, Guoming Li, Xiao Huang, Gui Cheng, Le Ma, Xiaodi Xu, Jiaxin Qian, Zhenfeng Shao

“…Forests play a crucial role in regulating the carbon balance and maintaining global climate stability. …”
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Performance Evaluation of Beluga Whale Optimization–Long Short-Term Memory–Random Forest Networks for Trajectory Control and Energy Optimization in Excavator Systems by Van Hien Nguyen, Kyoung Kwan Ahn

“…Simulations and experiments confirm that our approach achieves a positional error below 3 mm, ensuring precise tracking and providing reliable data for operators. …”
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New Emerging and Comprehensive Land Mapping Unit at Detailed Scale: Integrating Random Forest Analysis and Remote Sensing Techniques for Sustainable Land Management by Aditya Nugraha Putra, Reni Ustiatik, Novandi Rizky Prasetya, Erza Aulia Adara, Istika Nita, Syamsu Ridzal Indra Hadi, Soemarno Soemarno, Sudarto Sudarto, Sri Rahayu Utami, Mochammad Munir, Mochtar Lutfi Rayes

“…This study aims to present an innovative framework for the development of Land Mapping Units (LMUs) at a detailed scale (1:20,000), through the integration of Random Forest (RF) analysis and high-resolution remote sensing data. …”
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A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery by John B. Kilbride, Robert E. Kennedy

“…Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>ha</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Artificial neural networks and regression analysis for volume estimation in native species by Lucas M. Amorim, Elton da S. Leite, Deoclides R. de Souza, Liniker F. da Silva, Carlos R. de Mello, José M. de Lima

“…ABSTRACT Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. …”
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High-resolution canopy fuel maps based on GEDI: a foundation for wildfire modeling in Germany by Johannes Heisig, Milutin Milenković, Edzer Pebesma

“…Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. …”
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Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models by Daniel Fernando Figueiredo Spengler, Paulo Carteri Coradi, Dágila Melo Rodrigues, Izabela Cristina de Oliveira, Dalmo Paim de Oliveira, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…Abstract The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. …”
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Selection of geometrical features of nuclei оn fluorescent images of cancer cells by Ya. U. Lisitsa, M. M. Yatskou, V. V. Skakun, P. D. Pavel D. Kryvasheyeu, V. V. Apanasovich

“…For the selection of characteristics, the methods were used: median, correlation with calculation of the Pearson correlation coefficient, correlation with calculation of the Spearman correlation coefficient, logistic regression model, random forest with CART trees and Gini criterion, random forest with CART trees and error minimization criterion. …”
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Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach by Francisco-Javier Mesas-Carrascosa, Juan Tomás Arosemena-Jované, Susana Cantón-Martínez, Fernando Pérez-Porras, Jorge Torres-Sánchez

“…The results indicated that plot-scale NDVI estimation had the lowest error rates (1.4%) and the highest R² (0.89), outperforming the crop-scale model. …”
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Advancing Stem Volume Estimation Using Multi-Platform LiDAR and Taper Model Integration for Precision Forestry by Yongkyu Lee, Jungsoo Lee

“…Stem volume is a critical factor in managing and evaluating forest resources. At present, stem volume is commonly estimated indirectly by constructing a taper model that utilizes sampling, diameter at breast height (DBH), and tree height. …”
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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

“…Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in <i>Eucalyptus globulus</i> and <i>Pinus pinaster</i> stands in central and northern Portugal. …”
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Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms by Hande Küçükönder, Kubilay Kazım Vursavuş, Fatih Üçkardeş

“…In the classification processes performed according to these mechanical properties, K-Star, Random Forest and Decision Tree (C4.5) algorithms of data mining were utilized, and in the comparison of comprising classification models, Root Mean Square Error (RMSE), Mean absolute error (MAE), Root relative squared error (RRSE) and Relative absolute error (RAE) values, which are some of the criteria of error variance, were considered to be low, while the classification accuracy rate was considered to be high. …”
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A two-stage forecasting model using random forest subset-based feature selection and BiGRU with attention mechanism: Application to stock indices. by Shafiqah Azman, Dharini Pathmanathan, Vimala Balakrishnan

“…The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, [Formula: see text]. …”
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