Depth distributions of soil temperature: Seasonal sensitivity and simulation across dryness/wetness conditions by Yining Wang, Jiefeng Wu, Jian-yun Zhang, Tiesheng Guan, Guoqing Wang, Junliang Jin, Zhenlong Wang

“…Based on these patterns, the random forest model was employed to simulate the cumulative soil temperature (CST) during dry and wet periods, using the duration and severity of these periods at different soil depths as input variables. …”
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Assessing the Effects of Interchange Warning Systems on Driving Risk: A Driving Simulator Study by Jingyang Li, Fengxiang Guo, Wei Li, Tianxiang Xiao, Chengyu Hu

“…Random forest (RF) and SHapley Additive exPlanations (SHAP) are used to analyze the effects between driving behavior and driving risk change in both situations without and with IWS. …”
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Harnessing Digital Twin Technology for Detailed Simulation and Analysis of Climate Impact on Agricultural Systems by Hassan Muntather Muhsin, Ramadan Ghazi Mohamad, Karthikeyan R.

“…The model incorporates real time data collection and well developed, yet still new, algorithms like random forest to enable real time analysis, as well as realistic prediction using system simulation models to yield highly useful and actionable insights. …”
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An Integrated Model for Simulating Regional Water Resources Based on Total Evapotranspiration Control Approach by Jianhua Wang, Xuefeng Sang, Zhengli Zhai, Yang Liu, Zuhao Zhou

“…In this study, we developed a water allocation and simulation (WAS) model, which can simulate the water cycle and output different ET values for natural and artificial water use, such as crop evapotranspiration, grass evapotranspiration, forest evapotranspiration, living water consumption, and industry water consumption. …”
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Numerical simulation and experimental validation of the oleogel formation from grape seed oil and beeswax by Zohreh Baratian Ghorghi, Ali Faezian, Samira Yeganehzad, Mohammad Ali Hesarinejad, Alireza Sheikhi Darani, Marcello Fidaleo, Hashem Ahmadi Tighchi

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Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. …”
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Exploring the role of hepsin in prostate cancer: bioinformatics, molecular Docking and molecular dynamics simulations by Hongying Peng, Dan Du, Zhonggui Hu, Zhiliang Xia

“…Using advanced computational techniques such as weighted gene co-expression network analysis (WGCNA), Lasso regression, and random forest algorithms, we pinpointed key genes involved in tumorigenesis. …”
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An Integrated Global‐To‐Regional Scale Workflow for Simulating Climate Change Impacts on Marine Ecosystems by Kelly Ortega‐Cisneros, Denisse Fierros‐Arcos, Max Lindmark, Camilla Novaglio, Phoebe Woodworth‐Jefcoats, Tyler D. Eddy, Marta Coll, Elizabeth Fulton, Ricardo Oliveros‐Ramos, Jonathan Reum, Yunne‐Jai Shin, Cathy Bulman, Leonardo Capitani, Samik Datta, Kieran Murphy, Alice Rogers, Lynne Shannon, George A. Whitehouse, Ezekiel Adekoya, Beatriz S. Dias, Alba Fuster‐Alonso, Cecilie Hansen, Bérengère Husson, Vidette McGregor, Alaia Morell, Hem‐Nalini Morzaria Luna, Jazel Ouled‐Cheikh, James Ruzicka, Jeroen Steenbeek, Ilaria Stollberg, Roshni C. Subramaniam, Vivitskaia Tulloch, Andrea Bryndum‐Buchholz, Cheryl S. Harrison, Ryan Heneghan, Olivier Maury, Mercedes Pozo Buil, Jacob Schewe, Derek P. Tittensor, Howard Townsend, Julia L. Blanchard

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Geotechnical evaluation of embankment stability in seismic zones using Monte Carlo and subset simulations within an LRFD framework aided by machine learning by Furquan Ahmad, Pramod Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, Pijush Samui

“…Advanced computational modeling using three random forest-based models: RF-YYPO, RF-BWOA, and RF-SMA. …”
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Multi-scenario simulation of LULC and carbon dynamics in karst regions using the PLUS model by Caixia Ding, Caixia Ding, Zhongfa Zhou, Zhongfa Zhou, Zhongfa Zhou, Cui Wang, Cui Wang, Jie Kong, Jie Kong, Yanbi Wang, Yanbi Wang, Rukai Xie, Rukai Xie

“…Results show net carbon emissions increased 64.92% (from 121,558.73 to 200,477.90 tons), with built-up land becoming the main carbon source (167% growth) while forests contributed over 90% of carbon sequestration. …”
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Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau by Junpo Yu, Yajun Si, Wen Zhao, Zeyu Zhou, Jiming Jin, Wenjun Yan, Xiangyu Shao, Zhixiang Xu, Junwei Gan

“…Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. …”
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Hydrological and hydrodynamic coupling simulation under composite underlying surfaces in urban polder areas by Cheng Chen, Binquan Li, Yang Xiao, Huihui Li, Taotao Zhang, Dong Xu, Huanghao Yu

“…This study proposed a runoff and confluence model incorporating various land surfaces, including paddy fields, drylands, forests, urban areas, and water surfaces. The MIKE 11 model was used to simulate river flood evolution. …”
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Simulation and Forecast of Coastal Ecosystem Services in Jiaodong Peninsula Based on SSP-RCP Scenarios by Wenhui Guo, Ranghui Wang, Fanhui Meng

“…This study simulated the spatiotemporal changes in coastal ecosystem services (ESs) in the Jiaodong Peninsula from 2000 to 2050 and analyzed the driving mechanisms of climate change and human activities with respect to ESs, aiming to provide policy recommendations that promote regional sustainable development. …”
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Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets by D. Dalmonech, E. Vangi, M. Chiesi, G. Chirici, L. Fibbi, F. Giannetti, G. Marano, C. Massari, A. Nolè, J. Xiao, A. Collalti

“…Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. …”
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Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier by Ashutosh Kumar Singh, SVN Sreenivasu, U.S.B. K. Mahalaxmi, Himanshu Sharma, Dinesh D. Patil, Evans Asenso

“…The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. …”
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Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression by Muhammad Shahzad Nazir, Sami ud Din, Wahab Ali Shah, Majid Ali, Ali Yousaf Kharal, Ahmad N. Abdalla, Padmanaban Sanjeevikumar

“…This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). …”
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LATIS: A Sample of IGM-selected Protoclusters and Protogroups at z ∼ 2.5 by Andrew B. Newman, Mahdi Qezlou, Gwen C. Rudie, Nima Chartab, Guillermo A. Blanc, Daniel D. Kelson, Simeon Bird, Caitlin Casey, Enrico Congiu, Olga Cucciati, Denise Hung, Brian C. Lemaux, Victoria Pérez, Jorge Zavala

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Attributing the effects of climate change and forest disturbance on runoff using distributed modeling and indicators of hydrological alteration in Central European montane basins by Jakub Langhammer, Jana Bernsteinová

“…Study focus: This study examines the impacts of climate warming and forest disturbances on hydrological alterations in montane headwater basins. …”
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How to get closer to actual forest stand height using GEDI? A case study in central European Scots pine stands by Wojciech Krawczyk, Piotr Wężyk

“…We compared GEDI rhG parameters with several metrics commonly used as reference forest stand height (a series of Airborne Laser Scanning point cloud percentiles and relative height metrics of simulated waveforms; rhSW). …”
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Impact of wildfires on spatial and temporal evolution of groundwater recharge in an Atlantic pine forest: An integrated approach using field, remote sensing and modeling. by Mariana La Pasta Cordeiro, João Pedro Nunes, Maria Teresa Condesso de Melo

“…This study investigates extreme wildfires impacts on groundwater recharge in the Leiria Pine Forest, integrating field and remote sensing data with modelling tools to simulate recharge in burnt and unburnt areas from 2001 to 2023. …”
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