Quantifying links between aerosol optical depth and rapid urbanisation induced land use changes, Hangzhou, China, 2000–2020 by Zhifeng Yu, Zheyu Chen, Kun Sun, Bing Qi, Ben Wang, Haijian Liu, C.K. Shum, Wenshuang Guo, Huiyan Xu, Xiaohong Yuan, Bin Zhou

“…The regional study results show that the correlation between the operational AOD observations (MOD04_3K) at 3 km resolution and the in situ AOD measurements available in Hangzhou ranges from 0.48 to 0.80, and their root mean square error (RMSE) < 0.30. During the past two decades, Hangzhou has been in the stage of rapid urbanisation, and the area of construction land has increased significantly, mainly converted from cultivated land and forests. …”
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Parcel-scale crop planting structure extraction combining time-series of Sentinel-1 and Sentinel-2 data based on a semantic edge-aware multi-task neural network by Zhiguang Tang, Xiangdong Wang, Qin Jiang, Haizhu Pan, Gang Deng, He Chen, Yuanhong You, Sijia Li, Haiyan Hou

“…Results show: 1) SEANet achieves accurate extraction of parcel boundaries, extracting a total of 175,000 parcels, and demonstrates a relative error of 6.98% compared to statistical yearbook data. …”
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Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The Gradient Boosting model achieved the lowest error rate of 27%, indicating robust predictive capability. …”
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A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning by Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas

“…Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. …”
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Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study by Tran Thi Ngan, Tran Thi Ngan, Dang Huong Tra, Ngo Thi Quynh Mai, Hoang Van Dung, Nguyen Van Khai, Pham Van Linh, Nguyen Thi Thu Phuong, Nguyen Thi Thu Phuong

“…Among the models tested, the Extra Trees Regressor (ETR) demonstrated the highest predictive accuracy, achieving an R² of 87.37% and the lowest mean absolute error of 9.4 mcg (95% CI: 7.7–11.2) in the test set. …”
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An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R2 value of 0.76, which was 0.08–0.26 higher than the other methods. …”
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Unveiling the performance and influential factors of GEDI L2A for building height retrieval by Peimin Chen, Huabing Huang, Peng Qin, Zhenbang Wu, Zixuan Wang, Chong Liu, Na Dong, Jie Wang

“…While the Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) was primarily designed for forest measurements, it also holds potential for large-scale building height retrieval. …”
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Rapid retrieval of soil moisture using a novel portable L-band radiometer in the Hulunbeier Prairie, China by Shaoning Lv, Derek Houtz, Shiyuan Li, Yin Hu, Jing Zhang, Dongli Wu, Lei Jin, Jun Wen

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Ganoderma Disease in Oil Palm Trees Using Hyperspectral Imaging and Machine Learning by Chee Seng Kwang, Siti Fatimah Abdul Razak, Sumendra Yogarayan, M. Z. Adli Zahisham, Tze Huey Tam, M. K. Anuar Mohd Noor, Haryati Abidin

“…The developed band alignment significantly improved spectral coherence across the adjacent bands, as proved by enhanced Root Mean Squared Error (RMSE), Pearson Correlation Coefficient (PCC), and Normalized Mutual Information (NMI) values. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. …”
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Winter wheat plant density determination: Robust predictions across varied agronomic conditions using multiscale RGB imaging by Jara Jauregui-Besó, Adrian Gracia-Romero, Constanza S. Carrera, Marta da Silva Lopes, José Luis Araus, Shawn Carlisle Kefauver

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Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Additionally, the model demonstrated strong reliability in maintaining standardized prediction errors below 0.2 for most instruments, with probabilities of achieving this threshold being 96.43% for WHOQoL HIV Bref and 88.44% for ESTAR, while lower probabilities were observed for TSQM (44%) and WRFQ (51%).Conclusion: The results from our machine learning algorithms are promising for predicting PROMs and PREMs in AIDS settings. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. …”
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Integrating image segmentation and auxiliary data for efficient estimation of FVC and AGB by Xifeng Zhang, Lu Xu, Yaxiao Li, Ying Yang, Jianguo Li, Hongyuan Ma

“…Modeling the FVC of fresh and dry grass separately improves performance, increasing the R2 by an average of 0.23 and reducing the root mean square error (RMSE) by up to 41 %. Structural variables play a key role in AGB estimation, improving R2 by up to 0.28 and decreasing RMSE by 17 %. …”
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Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils by Ren-Min Yang, Lai-Ming Huang, Zhifeng Yan, Xin Zhang, Shao-Jun Yan

“…The QRF models, using these indices, achieved promising prediction accuracies, with a coefficient of determination (R2) of 0.54 and a root mean square error (RMSE) of 0.79 kg m−2 at the topmost 10 cm of soil. …”
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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…The findings indicate that RS is the most effective HPO technique across all models and highlight the significance of MACH-S and PHYS-B features in improving model predictions over PYRO-D features, achieving an R2 score of 0.948 and a mean-squared error (MSE) of 0.007. Finally, the feature-driven model incorporating optimal machine, pyrometer, and physical features was utilized for density prediction through ML. …”
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Modelling flood losses of micro-businesses in Ho Chi Minh City, Vietnam by A. Buch, A. Buch, A. Buch, D. Paprotny, K. Rafiezadeh Shahi, K. Rafiezadeh Shahi, H. Kreibich, N. Sairam

“…The models estimated the flood losses for HCMC's micro-businesses with a mean absolute error of 3.8 % for content losses (observed mean: 4.7 %, Q50: 0.0) and 18.7 % for business interruption losses (observed mean: 18.2 %, Q50: 10). …”
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