Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest by Shashika Himandi Gardeye Lamahewage, Chandi Witharana, Rachel Riemann, Robert Fahey, Thomas Worthley

“…Abstract Plants sequester carbon in their aboveground components, making aboveground tree biomass a key metric for assessing forest carbon storage. Traditional methods of aboveground biomass (AGB) estimation via Forest Inventory and Analysis (FIA) plots lack sufficient sampling intensity to directly produce accurate estimates at fine granularities. …”
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Height-Diameter Modeling of Cinnamomum tamala Grown in Natural Forest in Mid-Hill of Nepal by Mukti Ram Subedi, Bishwa Nath Oli, Surendra Shrestha, Sophan Chhin

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Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques by Roberto Molowny-Horas, Saeed Harati-Asl, Liliana Perez

“…Accurate modeling and simulation of forest land cover change resulting from epidemic insect outbreaks play a crucial role in equipping scientists and forest managers with essential insights. …”
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Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise by Shantaram B. Nadkarni, G. S. Vijay, Raghavendra C. Kamath

“…Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. …”
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Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests by Sara Winsemius, Chad Babcock, Van R. Kane, Kat J. Bormann, Hugh D. Safford, Yufang Jin

“…When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. …”
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A low resistance circular diverter tee based on an improved random forest model by Ao Tian, Angui Li, Ran Gao, Ruoyin Jing, Yi Wang, Yan Tian, Yibu Gao, Junkai Ren, Yingying Wang

“…This paper takes a tee as an example and proposes a novel resistance reduction method for building transmission and distribution systems that utilizes an improved random forest model. Unlike existing studies on local component resistance reduction that rely on trial-and-error empirical methods, this study introduces a posterior optimization approach that can obtain a global optimal solution within a given range. …”
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Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model by Siqi Li, Guyue Hu, Shaoda Li, Chenghao Liu, Xiao Wang, Ronghao Yang, Junxiang Tan

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An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…The results show that the Random Forest classifier demonstrates exceptional accuracy, achieving a 99% F1-score and an AUC of 100%. …”
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Aerial biomass allometric models for Prosopis affinis Spreng. in native Espinal forests of Argentina by Silvana M. J. Sione, Hernán J. Andrade-Castañeda, Silvia G. Ledesma, Leandro J. Rosenberger, José D. Oszust, Marcelo G. Wilson

“…ABSTRACT Estimation of carbon stored in forest ecosystems requires accurate biomass prediction tools. …”
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Mapping Tropical Forested Wetlands Biomass with LiDAR: A Machine Learning Comparison by Jonathan V. Solórzano, Candelario Peralta-Carreta, J. Alberto Gallardo-Cruz

“…The results indicate that the random forest model outperformed the others, achieving the lowest root mean squared error (<i>RMSE</i> = 20.25 Mg/ha, <i>rRMSE</i> = 12.25%, <i>R</i>² = 0.88). …”
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Development of a stereo vision-based UGV guidance system for bareroot forest nurseries by Sharif Shabani, Ashish R. Mulaka, Thomas A. Stokes, Tanzeel U. Rehman, Yin Bao

“…The US forest nursery industry still relies heavily on manual labor for inventories during the growing season. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data, based on the K-Nearest Neighbor (KNN) and Random Forest algorithms. Results The model conducted predictions on missing detection data and achieved indirect measurements of key indicators, with a relative error between predicted and measured values staying below 25%, meeting acceptable standards. …”
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Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest by Ao Zhang, Xiaohong Wang, Xin Gu, Xiangyao Xu, Xintong Gao, Linlin Jiao

“…Furthermore, the discrepancy between this estimation and the direct measurement outcomes of the forest management inventory (FMI) was minimal, exhibiting a relative error of only −5.2%, estimation precision of 94.8%. …”
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Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Pro... by Yuwen Peng, Huiyi Su, Min Sun, Mingshi Li

“…Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches, enhancing forest fire warnings and emergency response capabilities, and accurately budgeting potential carbon emissions resulting from fires. …”
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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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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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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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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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Speed Prediction of Urban Rail Transit Trains Based on Random Forest &amp; 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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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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