An improvement in ICESat-2 ATL03 snow depth estimation: a case study in the Tuolumne Basin, USA by Tongrui Chen, Lingmei Jiang, Jianwei Yang, Dan Kong, Yong Pang

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Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon by Moumita Saha, Arun Chakraborty, Pabitra Mitra

“…Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. …”
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Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision by Shuxin Zhong, Li Cheng, Haiwen Yuan, Xuan Li

“…To improve the accuracy of Wi-Fi positioning, a random forest algorithm with added region restriction is proposed. …”
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Hybrid retrieval of grass biophysical variables based-on radiative transfer, active learning and regression methods using Sentinel-2 data in Marakele National Park by Philemon Tsele, Abel Ramoelo

“…Results show the most accurate grass LAI and LCC retrievals had lower relative root mean squared errors (RRMSEs) of 39.87% and 16.58% respectively. …”
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Uncertainty‐Aware Machine Learning Bias Correction and Filtering for OCO‐2: 1 by Steffen Mauceri, William Keely, Josh Laughner, Christopher W. O’Dell, Steven Massie, Robert Nelson, David Baker, Matthäus Kiel, Otto Lamminpää, Jonathan Hobbs, Abhishek Chatterjee, Tommy Taylor, Paul Wennberg, Sean Crowell, Britton Stephens, Vivienne H. Payne

“…We propose a Random Forest (RF), a non‐linear, interpretable machine learning (ML) technique, to correct these biases. …”
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Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations by J. Xia, L. Guan

“…Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. …”
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Use of modified snares to estimate bobcat abundance by Heather K. Stricker, Jerrold L. Belant, Dean E. Beyer Jr., Jeanette Kanefsky, Kim T. Scribner, Dwayne R. Etter, Jean Fierke

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From Herbarium to Landscape: New Records and Mapping Rare and Threatened Species of Brazilian Atlantic Rainforest by Otávio Miranda Verly, Luiz Claudio Medeiros Cabral‐da‐Silva, Marcos Sobral, Klisman Oliveira, Laura Beatriz Assis Teixeira, Maria Paula Miranda Xavier Rufino, Aline Ferreira deMendonça, Kesleyane Pereira Camilo, Carlos Moreira Miquelino Eleto Torres

“…Atlantic Rainforest of Minas Gerais state, Brazilian southeastern. We used multi‐level forest inventory data from 137 plots across nine Semideciduous Seasonal Forest fragments, sampled 1–9 times over 30 years. …”
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CIA-UNet: An Attention-Enhanced Multi-Scale U-Net for Single Tree Crown Segmentation by Jiapeng Duan, Chuanzhao Tian, Wansi Liu, Lixiang Cao, Teng Feng, Xiaomin Tian

“…Accurate segmentation of single tree crowns enables precise measurement of tree height, DBH, and stock volume, facilitating effective assessment of forest growth and productivity. However, single tree crown segmentation currently faces several challenges, including insufficient segmentation accuracy, mis-segmentation and crown adhesion. …”
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Spatial sample weighted machine learning for multitemporal land cover change modeling with imbalanced datasets by Alysha van Duynhoven, Suzana Dragićević

“…The RF-SSW, NN-SSW, and XGB-SSW models forecasted more realistic changes across multiple timesteps with fewer errors than baseline configurations. The presented methodology provides a step toward establishing spatialized cost-sensitive learning strategies and extending classical ML models to multitemporal LC datasets.…”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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Experimental-Numerical Study of Indexation of Scenic Road Vertical Alignment in China by Ronghua Wang, Xingliang Liu, Zhe Yuan

“…According to verification results, relative errors of climbing velocity vary from 0.0104 to 0.0205, showing the dynamic model’s accuracy and further proving the practicality of MLS and LSL values obtained. …”
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Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset by Ali Suliman AlSalehy, Mike Bailey

“…Machine learning algorithms like Isolation Forest and Local Outlier Factor were also used, chosen for their robustness to non-normal data distributions, significantly improving subsequent imputation accuracy. …”
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Integrating system dynamics and machine learning for environmental impact analysis of building materials in the demolition process by Babatunde Oluwaseun Ajayi, Mayowa Emmanuel Bamisaye, Hugo Miguel Magalhães Macedo, Issara Sereewatthanawut

“…This study integrates System Dynamics (SD) modeling with the Random Forest (RF) algorithm to analyze the relationships among key variables and assess the overall environmental impacts of different demolition tool combinations (OEIC1, OEIC2, OEIC3, and OEIC4). …”
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LC oscillator frequency prediction using machine learning linear regression algorithm by Mandar Jatkar, Vasudeva G, Tripti R. Kulkarni, Roopa R. Kulkarni, Aneesh Pandurangi

“…It was found that Linear Regression reaches close to zero RMSE, but SVR and Random Forest show higher errors. The study proves that log-transformed linear regression works as a basic but efficient method for accurate frequency estimation in resonant LC circuits.…”
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Problems of modelling carbon biogeochemical cycle in agricultural landscapes by O.E. Sukhoveeva

“…The latter ones fall into carbon cycle models (agroecosystem, phytocenosis, and greenhouse gases (СО2 and СН4) emissions) and carbon-nitrogen cycle models (various ecosystems, forest, and microbiological). The following difficulties arising when mathematical methods are used for description of the carbon cycle were discussed: multiple methods of calculation; high requirements to input data; limited availability of input information; necessity to consider climate change; errors in description of the functional dependence of CO2 emission on temperature. …”
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Patient Safety Culture Among Healthcare Settings in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis by Natnael Atnafu Gebeyehu, Kirubel Tegegne, Biruk Admass, Nathan Shewangashaw, Yibeltal Atalay, Dagne Sewuyew, Awoke Gebremariam, Kelemu Abebe Gelaw

“…To evaluate publication bias, methods such as Egger's regression tests, rank tests, and forest plots were utilized. The I2 statistic was used to assess heterogeneity, followed by an overall estimated analysis. …”
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Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction by İrem Üstek, Miguel Arana‐Catania, Alexander Farr, Ivan Petrunin

“…The first used a deep autoencoder to obtain latent features, which were then fed into clustering models, isolation forest, local outlier factor and one‐class support vector machines for anomaly detection. …”
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation by Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, Zainab Al-Khafaji

“…In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. …”
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Improving the prediction of bitumen’s density and thermal expansion by optimizing artificial neural networks with Optuna and TensorFlow by Eli I. Assaf, Xueyan Liu, Sandra Erkens

“…Previous work demonstrated that Random Forest Regressors (RFRs) could estimate the physical properties of bitumen using molecular descriptors derived from Molecular Dynamics (MD) simulations, thereby reducing the need for computationally intensive simulations. …”
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