The impact of fractional cover distribution in training samples on the accuracy of fractional cover estimation: a model-based evaluation by Rujia Wang, Chen Shi

“…Fractional cover estimation was performed using random forest regression, with accuracy assessed on an independent test set. …”
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Assessment of geotechnical behavior of gypseous soil under leaching effect using machine learning by Saif M. Hassan Al-Riahi, Nur Irfah Mohd Pauzi, Mohammed Y. Fattah, Hasan Ali Abbas

“…Three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR)—were trained to predict leaching strain, a critical indicator of collapse potential. …”
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Enhancing Energy Efficiency of Sensors and Communication Devices in Opportunistic Networks Through Human Mobility Interaction Prediction by Ambreen Memon, Sardar M. N. Islam, Muhammad Nadeem Ali, Byung-Seo Kim

“…In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. …”
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Cascading Landslide–Barrier Dam–Outburst Flood Hazard: A Systematic Study Using Rockfall Analyst and HEC-RAS by Ming Zhong, Xiaodi Li, Jiao Wang, Lu Zhuo, Feng Ling

“…First, landslide susceptibility is assessed through a random forest model incorporating 11 static environmental and geological factors. …”
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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

“…Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. …”
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Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods by Zuopeng Zhang, Zhe Li, Zhirong Li

“…Three classification models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accuracy of land use classification. …”
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Life cycle emissions associated with vault storage of wood cleared for fire management in the Western United States by Declan Johnson, Jimmy Voorhis, Stephen Porder

“…Abstract Background Climate change, fire suppression, and human encroachment contribute to increasingly intense forest fires in the Western United States, releasing hundreds of millions of metric tons (MMT) CO2/year. …”
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Cold Hardiness and Physio-Biochemical Responses of Annual Branches in Five Early-Fruiting Walnut Varieties (<i>Juglans regia</i> L.) Under Simulated Low-Temperature Stress by Zitong Ni, Haifang Hu, Guiqing Xu

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Effects of pH, temperature and hydraulic disturbance on nitrogen release from sediments in the Sunxi River, Three Gorges Reservoir Area, China by Yihong Ning, Bin Gao, Haiyan Wang, Wenning Hou

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Anthropogenic Habitat Loss and Fragmentation May Alter Coevolutionary Progress as Examined in a Brood Parasitism Model by Wei Wang, Timothy Van Deelen, Fuwen Wei, Sheng Li, Luping Wang

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Integrated Forecast of Monthly Saltwater Intrusion at Modaomen Waterway Based on Multiple Models by LU Pengyu, LIN Kairong, YANG Yugui, YUAN Fei, HE Yong

“…This paper builds the regression model by Random Forest (RF) algorithm, Support Vector Machine (SVM) and Elman Neural Network (ENN), and conducts a monthly integrated forecast through Bayesian Model Averaging (BMA) method. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…Based on the unit velocity space, the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models, while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles. …”
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Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses by Esra Imamoglu

“…To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. …”
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Explainable Model Prediction of Memristor by Sruthi Pallathuvalappil, Rahul Kottappuzhackal, Alex James

“…System level simulation of neuro-memristive circuits under variability are complex and follow a black-box neural network approach. …”
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Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images by Yujun Yi, Kebing Chen, Jiaxin Xu, Qiyong Luo

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Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches by Said El Harkaoui, Cristina Ortiz Cruz, Aaron Roggenland, Micha Schneider, Sascha Rohn, Stephan Drusch, Bertrand Matthäus

“…The MC-simulated data were then used to simulate larger datasets, a critical step for training and testing two classification models: Random Forest (RF) and Neural Networks (NN), as robust training cannot be achieved with small sample sizes. …”
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Integrating Temporal Vegetation and Inundation Dynamics for Elevation Mapping Across the Entire Turbid Estuarine Intertidal Zones Using ICESat-2 and Sentinel-2 Data by Siqi Yao, Jianrong Zhu, Wanying Zhang, Bo Tian, Weiwei Sun, Weiguo Zhang, Weiming Xie, Pengjie Tao, Chunpeng Chen, Kai Tan

“…This method utilizes a random forest (RF) to model the relationships between elevations from Ice, Cloud, and Elevation Satellite 2 (ICESat-2) and band, texture, and index features from Sentinel-2, without relying on any supplementary in situ measurements. …”
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Analysis of material flow and energy flow in collaborative pyrolysis of Chinese medicine residue by WEI Chuyun, ZHANG Jintai, LIU Guoqing, HUANG Zirui, WANG Bochun, YANG Haiwei, ZHOU Aijiao*

“…The actual pyrolysis process data of waste pine wood from agricultural forest source were systematically simulated, the material flow and energy flow during the pyrolysis process of the sargentgloryvine stem medicine residue was analyzed, and the conversion efficiency was comprehensively evaluated. …”
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Effects of land use change on ecosystem service value under different scenarios in the Guangdong-Hong Kong-Macao Greater Bay area by Xiaohui Ding, Ji Yang, Gen Zhang, Lingjia Liu, Quan Liu, Ruiyou Li, Jia Hu

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BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes by Thanadol Tuntiwongwat, Sippawit Thammawiset, Thongchai Rohitatisha Srinophakun, Chawalit Ngamcharussrivichai, Somboon Sukpancharoen

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