Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…The findings highlight a critical temporal transferability gap, especially for the TR parameter, limiting the current operational readiness of standard ML models. …”
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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

“…This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. Bayesian optimization was used to determine the best-fit hyperparameters, resulting in RMSE, MAE, and R² values ranging from 0.923 to 2.497 m, 0.709–2.132 m, and 0.057–0.914, respectively, during model validation. …”
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Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China by Jun Zhu, Zhong-Xiu Sun

“…A linear model was fitted to enhance the current calculation method, resulting in the equation: <i>CEC_clay</i> = 15.31 + 15.90 × (<i>CEC_soil</i>/<i>Clay</i>), with an R² of 0.41 and RMSE of 4.48 cmol(+) kg⁻¹. …”
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Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…Experimental results showed that the LTCA–TCN algorithm outperforms other comparative algorithms in root mean square error (RMSE) and average Score metrics. It achieves the best RMSE and Score across all three phases. …”
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Large-scale ecological engineering increases forest canopy height in Loess Plateau from 1985 to 2020 by Mengxue Liu, Lin Ma, Zhoutao Zheng, Nan Cong, Guang Zhao, Yangjian Zhang, Bingyu Zhao, Li Liu, Daijun Yao, Xiaoqing Duan

“…The results indicate that our FCH product shows good agreement with ground validation (Mean FCH R2 = 0.744, RMSE = 3.993; Maximum FCH R2 = 0.705, RMSE = 4.535). …”
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Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data by Yisa Li, Dengsheng Lu, Yagang Lu, Guiying Li

“…Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. …”
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Short-term train arrival delay prediction: a data-driven approach by Qingyun Fu, Shuxin Ding, Tao Zhang, Rongsheng Wang, Ping Hu, Cunlai Pu

“…Purpose – To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. …”
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Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie, Li Yan

“…The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (<i>R</i>²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. …”
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Analyzing the applicability of psychometric QoE modeling for projection-based point cloud video quality assessment by Sam Van Damme, Jeroen van der Hooft, Filip De Turck, Maria Torres Vega

“…Thus, real-time quality monitoring and prediction mechanisms are key to allow for fast countermeasures in case of QoE decrease. Since current state-of-the-art research is focusing on either continuous QoE monitoring of traditional video streaming services or objective delivery optimizations of point cloud content without any QoE validation, we believe this work brings a valuable contribution to current literature. …”
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Deep Learning Model on Energy Management in Grid-Connected Solar Systems by V. Senthil Nayagam, A. P. Jyothi, P. Abirami, J. Femila Roseline, M. Sudhakar, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, N. Hoda, Asefa Sisay

“…The current state information from the battery, as well as control objectives, is used in this study to design control actions that maximise the amount of electricity injected into the grid. …”
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A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools by Saad Javed Cheema, Masoud Karbasi, Gurjit S. Randhawa, Suqi Liu, Travis J. Esau, Kuljeet Singh Grewal, Farhat Abbas, Qamar Uz Zaman, Aitazaz A. Farooque

“…A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. …”
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State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng, Yujie Zhou

“…Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. …”
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Application of Seq2Seq models for predicting the development of thunderstorm activity to enhance the pilot’s situational awareness in flight by G. V. Kovalenko, I. A. Yadrov

“…Application of the proposed thunderstorm activity forecasting technology can enhance the situational awareness of the flight crew improving the projection of the current situation into the near future and optimizing the decision-making process for thunderstorm avoidance by providing crew members with predictive information about thunderstorm development on the navigation display screen. …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Results indicate that (1) the proposed paradigm achieves optimal SOC content prediction accuracy in humid regions, with a root mean square error (RMSE) of 3.58 g kg−1, a coefficient of determination (R2) of 0.76, a ratio of performance to interquartile distance (RPIQ) of 2.26, and a mean absolute error (MAE) of 4.73 g kg−1. …”
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Estimation of aboveground biomass in Tajikistan based on upscaling extrapolation of UAV and Sentinel-2 multi-source data synergy by Lina Hao, Huping Ye, Shuang He, Xinyu Zhang, Dalai Bayin, Mustafo Safarov, Mekhrovar Okhonniyozov, Xiaohan Liao

“…Grasslands constitute the largest terrestrial ecosystem, currently sequestering significant amounts of atmospheric carbon and playing a critical role in climate change mitigation and the global carbon cycle. …”
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Edge-Fog Computing-Based Blockchain for Networked Microgrid Frequency Support by Ying-Yi Hong, Francisco I. Alano, Yih-der Lee, Chia-Yu Han

“…The root mean square error (RMSE) of the current obtained using the traditional model predictive control (MPC) and the proposed LSTM-MFPC applied to the inverter are 0.1970 and 0.1432, respectively. …”
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Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

“…An economic analysis revealed a significant reduction in water treatment costs with the optimized system. The current work extends these experimental insights through XG-Boost to predict productivity, employing evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Variation of the Root Mean Squared Error (CVRMSE), and the determination coefficient (R2), with resulted values denoted as 0.43708%, 0.95879%, 0.2780%, 0.05290%, 12.2078%, and 0.88144% respectively. …”
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Accurate and Robust Train Localization: Fusing Degeneracy-Aware LiDAR-Inertial Odometry and Visual Landmark Correction by Lin Yue, Peng Wang, Jinchao Mu, Chen Cai, Dingyi Wang, Hao Ren

“…To overcome the limitations of current train positioning systems, including low positioning accuracy and heavy reliance on track transponders or GNSS signals, this paper proposes a novel LiDAR-inertial and visual landmark fusion framework. …”
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Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

“…Despite advances in electronic medical records, mobile health applications, and wearable sensors, current digital health cannot fully exploit these data due to the lack of data analysis and exchange between heterogeneous systems. …”
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Design of mTCN framework for disaster prediction a fusion of massive machine type communications and temporal convolutional networks by M. Umadevi, J. Arun Kumar, S. Vishnu Priyan, C. Vivek

“…Abstract Natural disasters such as floods, tsunamis, and earthquakes significantly impact lives and infrastructure, highlighting the urgent need for accurate and real-time prediction systems. Current methods often suffer from limitations in scalability, privacy, and real-time data integration, particularly in large-scale disaster scenarios. …”
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