Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia by Ayesh Dushmantha, Ruixuan Zhang, Yilin Gui, Jinjiang Zhong, Chaminda Gallage

“…Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. …”
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Performance and emission prediction using ANN (artificial neural network) on H2-assisted Garcinia gummi-gutta biofuel doped with nano additives by Harish Venu, Manzoore Elahi M. Soudagar, Tiong Sieh Kiong, N. M. Razali, Hua-Rong Wei, T. M. Yunus Khan, Naif Almakayeel, M. A. Kalam, Erdem Cuce

“…Abstract The current work focuses on utilization of ANN (artificial neural network) for the prediction of performance and tailpipe emissions of Garcinia gummigutta methyl ester (GGME) enriched with H2 and TiO2 nano additives. …”
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Preliminary performance analysis using the PPP-RTK service of the National Land and Mapping Center in Taiwan by J.-Y. Lin, C.-H. Chu, F.-Y. Chu, K.-W. Chiang, M.-L. Tsai

“…Although the accuracy of the NLSC PPP-RTK service currently falls below TerraStar's, the system is still in its early development stages. …”
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Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling by Xuebin Xie, Chuanqi Huang

“…Currently, the central task of predicting rock fragmentation is becoming increasingly important in the field of rock mechanics and engineering blasting. …”
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Predictive Modeling of soil salinity integrating remote sensing and soil variables: An ensembled deep learning approach by Sana Arshad, Jamil Hasan Kazmi, Endre Harsányi, Farheen Nazli, Waseem Hassan, Saima Shaikh, Main Al-Dalahmeh, Safwan Mohammed

“…From this perspective, the current research aimed to predict soil electrical conductivity (EC) from remote sensing and soil data using advanced deep learning (DL) architectures. …”
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A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis by Ji Li, Xiangyu Yan, Xingjie Chu, Ying Zhang, Guoliang Liu, Lin Li, Yue Li, Xiaochun Dong, Zihan Mei, Zhengkun Liu, Jinyue Yuan, Xiaohan Sun, Chunxia Cao

“…Future work will further optimize these models for more timely and accurate predictions, enhancing public health responses.…”
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UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation by Lulu Zhang, Xiaowen Wang, Huanhuan Zhang, Bo Zhang, Jin Zhang, Xinkang Hu, Xintong Du, Jianrong Cai, Weidong Jia, Chundu Wu

“…Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. …”
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Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain by Zhenghao Zhang, Gensheng Dou, Xin Zhao, Yang Gao, Saisai Liu, Anzhen Qin

“…Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R²) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. …”
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Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements by Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom

“…Various hyperparameters were optimized, and data preprocessing and separation were conducted to optimize the model. …”
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Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning by Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu, Hao Yang

“…Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model accuracy. …”
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Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. …”
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Research on Multi-Factor Coastal Waterway Depth Prediction and Application Based on Attention-Enhanced LSTM Model by LING Ganzhan, HAN Yu, WANG Jiawei, JIE Weiwei, TANG Ruikai, HU Jiakai, LIU Xiang, LIANG Guangyue, CAO Lu, LIANG Ming

“…During the dry season, MAE is reduced by 64.67%, and in the wet season, it decreases by 72.37%. The RMSE is also reduced by 67.52% and 73.39% in the respective seasons, with the R² coefficient improving by 2.18% and 5.60%. …”
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EVALUATION OF ITERATIVE ALGORITHMS FOR TOMOGRAPHY IMAGE RECONSTRUCTION by Alexandre F. Velo, Alexandre G. Alvarez, Margarida Mizue Hamada, Carlos Henrique de Mesquita

“…The analyses involved the measurement of the contrast to noise ratio (CNR), the root mean square error (RMSE) and the Modulation Transfer Function (MTF),in order to know which algorithm fits the conditions to optimize the system better.   …”
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Modeling of CO₂ Efflux from Forest and Grassland Soils Depending on Weather Conditions by Sergey Kivalov, Irina Kurganova, Sergey Bykhovets, Dmitriy Khoroshaev, Valentin Lopes de Gerenyu, Yiping Wu, Tatiana Myakshina, Yakov Kuzyakov, Irina Priputina

“…The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R²) were employed to assess the quality of the model’s performance. …”
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Design and Prototype Verification of a 3-meter Aperture Wrap-rib Reflector by ZHANG Han, YAN Zhongxi, XIANG Ping, WU Minger

“…The shape of the lenticular tube wrap-rib was optimized by combining the form-finding analysis of the flexible reflector with the genetic algorithm. …”
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Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion by Jiaqi Duan, Hong Wang, Yuhang Yang, Mingwang Cheng, Dan Li

“…The results indicate the following: (1) The fusion of satellite and UAV images, combined with spectral information and textural features, can significantly improve the estimation accuracy of LAI and SPAD compared to using only spectral information or textural features. (2) Sparrow search algorithm-optimized extreme gradient boosting (SSA-XGBoost) regression achieved the highest accuracy, with R² and RMSE of 0.904 and 0.183 in LAI estimation and 0.857 and 0.882 in SPAD estimation, respectively. …”
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Efficient Multi-Threaded Data Starting Point Matching Method for Space Target Cataloging by Jiannan Sun, Zhe Kang, Zhenwei Li, Cunbo Fan

“…Currently, multi-target survey telescope arrays play an important role in the build-up and maintenance of space object catalog databases, collecting massive observational data without attributing information. …”
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Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang, Yu Zhang

“…This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. …”
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Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil by Lorena-De Arriba-Rodríguez, Francisco Ortega-Fernández, Joaquín M. Villanueva-Balsera, Vicente Rodríguez-Montequín

“…Corrosion is one of the main concerns in the field of structural engineering due to its effect on steel buried in soil. Currently, there is no clearly established method that allows its calculation with precision and ensures the durability of this type of structures. …”
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Investigation into the Prediction of Ship Heave Motion in Complex Sea Conditions Utilizing Hybrid Neural Networks by Yuchen Liu, Xide Cheng, Kunyu Han, Zhechun Liu, Baiwei Feng

“…While navigating at sea, ships are influenced by various factors, including wind, waves, and currents, which can result in heave motion that significantly impacts operations and potentially leads to accidents. …”
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