Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani

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Spilt Tensile Strength of Fiber-Reinforced Recycled Aggregate Concrete Simulation Employing Tunned Random Forests Trees by Xiao Chen, Chen Xi, Min Xiao, Lang Wu

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Quantitative assessment method for firefighting danger based on numerical simulation of forest fire spread in canyon wind fields. by Ao Wang, Chenghu Wang, Guiyun Gao, Ningyu Wu, Haiyan Su

“…Forest firefighting incidents frequently occur in mountainous and canyon regions which are characterized by complex topography, primarily because of variable local wind patterns that create conditions conducive to the spread of forest fires. …”
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Functional Trait Trade-Offs for the Tropical Montane Rain Forest Species Responding to Light from Simulating Experiments by Peili Mao, Runguo Zang, Hongbo Shao, Junbao Yu

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Net Primary Production Simulation and Influencing Factors Analysis of Forest Ecosystem Based on a Process-Based Model by Zhu Yang, Xuanrui Huang, Yunxian Qing, Hongqian Li, Libin Hong, Wei Lu

“…Accurate assessment of net primary production (NPP) can truly reflect the carbon budget balance of the forest ecosystem. In this study, the boreal ecosystem productivity simulation (BEPS) model was used to simulate the NPP of Saihanba mechanized forest farm in 2020, and the influencing factors of NPP were analyzed. …”
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Quantitative assessment method for firefighting danger based on numerical simulation of forest fire spread in canyon wind fields by Ao Wang, Chenghu Wang, Guiyun Gao, Ningyu Wu, Haiyan Su

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Enhanced simulation of gross and net carbon fluxes in a managed Mediterranean forest by the use of multi-sensor data by Marta Chiesi, Nicola Arriga, Luca Fibbi, Lorenzo Bottai, Luigi D'Acqui, Alessandro Dell’Acqua, Sara Di Lonardo, Lorenzo Gardin, Maurizio Pieri, Fabio Maselli

“…The current paper presents the last advancements introduced into a modelling strategy capable of simulating gross and net forest carbon (C) fluxes, i.e. gross and net primary and net ecosystem production (GPP, NPP and NEP, respectively). …”
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Simulated Herbivory Induces Volatile Emissions of Oak Saplings, but Parasitoid Communities Vary Mainly Among Forest Sites by Freerk Molleman, Priscila Mezzomo, Manidip Mandal, Soumen Mallick, Martin Moos, Petr Vodrážka, Andreas Prinzing, Urszula Walczak

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Climate change threatens old-growth forests in the Northern Alps by Rupert Seidl, Dominik Thom, Sebastian Seibold, Michael Maroschek, Werner Rammer

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Simulation of granular flows and machine learning in food processing by X. Cui, D. Adebayo, H. Zhang, M. Howarth, A. Anderson, T. Olopade, K. Salami, S. Farooq

“…Based on the simulation data, we apply machine learning techniques such as Random Forest, Linear Regression, and Ridge Regression to evaluate the effectiveness of these models in predicting granular flow patterns. …”
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Remote sensing and ecosystem modeling to simulate terrestrial carbon fluxes by Sergio Sánchez-Ruiz

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Simulations reveal variability in exposure to drier conditions during timing of budbreak for tree species of the mixedwood forests of Québec, Canada by Benjamin Marquis

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Full-Wave Simulations of Forest at L-Band With Fast Hybrid Multiple Scattering Theory Method and Comparison With GNSS Signals by Jongwoo Jeong, Leung Tsang, Mehmet Kurum, Abesh Ghosh, Andreas Colliander, Simon Yueh, Kyle McDonald, Nicholas Steiner, Michael H. Cosh

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Streamflow Intermittence in Europe: Estimating High‐Resolution Monthly Time Series by Downscaling of Simulated Runoff and Random Forest Modeling by Petra Döll, Mahdi Abbasi, Mathis Loïc Messager, Tim Trautmann, Bernhard Lehner, Nicolas Lamouroux

“…Interannual variations of the number of non‐perennial months at non‐perennial reaches were satisfactorily simulated, with a median Pearson correlation of 0.5. …”
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Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets by Sheng Liu, Conghao Liu, Xunan An, Xin Liu, Liang Hao

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What Drives the H i Content of Central Galaxies—A Comparison between Hydrodynamic Simulations and Observations Using Random Forest by Xiao Li, Cheng Li, H. J. Mo

“…We quantify the correlations of M _H _i / M _* with a variety of galaxy properties using the Random Forest regression technique, and we make comparisons between the two simulations, as well as between the simulations and xGASS. …”
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Experimental study on in-situ simulation of rainfall-induced soil erosion in forest lands converted to cash crop areas in Dabie Mountains. by Gao Li, Tao Yang, Rui Chen, Haogang Dong, Feng Wu, Qinghua Zhan, Jinyan Huang, Minxuan Luo, Li Wang

“…Therefore, this study was designed to reveal the evolution characteristics of rainfall-induced slope erosion and the key influencing factors in the forest land converted to cash crop area in Dabie Mountains. …”
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Wheat Production Simulation Using Sentinel 2 Images and Machine Learning Techniques by H. Ramezani Etedali, M. Ahmadi

“…Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…To address the high computational cost resulting from the complex mathematical expressions of traditional high-order yield criteria, this study proposes a data-driven modeling approach for high-order yield criteria aimed at improving computational efficiency in sheet metal forming simulations. 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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Digital mapping of soil cover eroded patterns on the basis of soil erosion simulation model (northern forest-steppe of the Central Russian Upland) by D. N. Kozlov, A. P. Zhidkin, Nikolay Igorevich Lozbenev

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