Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques by Ahmet Durap

“…Using a dataset spanning 2010 to 2022, the study employs wave characteristics such as maximum wave height (Hmax), wave periods (Tz, Tp), peak direction, and sea surface temperature (SST) as predictors. …”
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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

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Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B₄C/GNPs Hybrid Composites by Dhanunjay Kumar Ammisetti, Bharat Kumar Chigilipalli, Baburao Gaddala, Ravi Kumar Kottala, Radhamanohar Aepuru, T. Srinivasa Rao, Seepana Praveenkumar, Ravinder Kumar

“…The main aim of the study is to study the effect of various wear parameters (reinforcement percentage (R), applied load (L), sliding distance (D), and velocity (V)) on the wear characteristics (wear rate (WR)) of the AZ91/B₄C/GNP composites. …”
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Exposure-Sensitivity-Adaptability (ESA) framework based evaluation and characteristics analysis of urban disasters vulnerability in China by Rui Zhang, Anli Jiang, Linlin Jiang

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems by Ahmed Al Hammadi, Lina Bariah, Sami Muhaidat, Mahmoud Al-Qutayri, Paschalis C. Sofotasios, Merouane Debbah

“…However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. …”
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Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique by Shohreh Jalali, Majid Baniadam, Morteza Maghrebi

“…The impedance characteristics of multi-walled carbon nanotube (MWCNT)/polystyrene nanocomposites synthesized via microwave-assisted in-situ polymerization were systematically investigated to determine the effects of microwave power, exposure time, and frequency on impedance properties. …”
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GF-2 Remote Sensing-Based Winter Wheat Extraction With Multitask Learning Vision Transformer by Zhihao Zhao, Zihan Liu, Heng Luo, Hui Yang, Biao Wang, Yixin Jiang, Yanqi Liu, Yanlan Wu

“…Furthermore, the normalized difference vegetation index (NDVI) and land surface temperature (LST) derived from Landsat 8 images was included to enhance the representation of winter wheat's spectral characteristics. …”
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Bio-Signal-Guided Robot Adaptive Stiffness Learning via Human-Teleoperated Demonstrations by Wei Xia, Zhiwei Liao, Zongxin Lu, Ligang Yao

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Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-... by Bhaskar Mandal

“…Additionally, it seeks to understand the evolving pattern of land surface temperature and its correlation with biophysical characteristics in the Brahmani-Dwarka interfluve region from 1991 to 2021. …”
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Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

“…The finite element platform FssiCAS is employed for a computational analysis to study the characteristics of seismic subsidence in saturated seabed foundations with interbedded soft clay–sand. …”
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Hull form optimization of fully parameterized small ships using characteristic curves and deep neural networks by Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo

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A deep learning-based reconstruction model for 3D sound speed field combining underwater vertical information by Hongchen Li, Yuhang Liu, Ming Li, Penghao Wang, Yuhang Zhu, Kefeng Mao, Xi Chen

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Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring by Xinao Lin, Fengcai Lei, Xiu Liang, Yang Jiao, Xiaofei Zhao, Zhen Li, Chao Zhang, Jing Yu

“…By incorporating Raman signal intensity, coffee ring diameter, and POD as combined features, we established a machine learning-based mapping between nanoplastic concentration and coffee ring characteristics, allowing precise predictions of dispersion concentration. …”
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Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India by Abhijeet Das

“…For its purpose, by employing innovative techniques, such as Methods Based on Removal Effects of Criteria (MEREC/Me) Water Quality Index (WQI), Multi-Criteria Decision-Making analysis namely Additive Ratio Assessment (ARAS) modeling and Machine Learning approaches entitled as Random Forest (RF) technique, the present study identifies locations, which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface runoff, which lead to water quality variability in a water body over a monitoring period. …”
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On the machine learning algorithm combined evolutionary optimization to understand different tool designs’ wear mechanisms and other machinability metrics during dry turning of D2... by Muhammad Sana, Muhammad Umar Farooq, Sana Hassan, Anamta Khan

“…Secondly, the open-source supervised machine learning architecture search as carried out to map process characteristics such as surface evolution, tool wear, and chip morphology (width and thickness). …”
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Unravelling the Characteristics of Microhabitat Alterations in Floodplain Inundated Areas Based on High-Resolution UAV Imagery and Remote Sensing: A Case Study in Jingjiang, Yangtz... by Yichen Zheng, Dongshuo Lu, Zongrui Yang, Jianbo Chang

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MOMFNet: A Deep Learning Approach for InSAR Phase Filtering Based on Multi-Objective Multi-Kernel Feature Extraction by Xuedong Zhang, Cheng Peng, Ziqi Li, Yaqi Zhang, Yongxuan Liu, Yong Wang

“…This research provides important insights into the application of deep learning for InSAR denoising.…”
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Aftershock Spatiotemporal Activity and Coseismic Slip Model of the 2022 Mw 6.7 Luding Earthquake: Fault Geometry Structures and Complex Rupture Characteristics by Qibo Hu, Hongwei Liang, Hongyi Li, Xinjian Shan, Guohong Zhang

“…A machine learning approach is applied to extract a high-quality aftershock catalog from the original seismic waveform data, enabling the analysis of the spatiotemporal characteristics of aftershock activity. …”
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Surface wettability analysis using a microdroplet: a numerical approach by Ganesh Meshram, Gloria Biswal, Ashish Khelkar

“… Analysis of hydrophobicity is essential for learning about the characteristics of molecules, surfaces, and materials that reject water. …”
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A Global Probability‐Of‐Fire (PoF) Forecast by J. R. McNorton, F. Di Giuseppe, E. Pinnington, M. Chantry, C. Barnard

“…Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data‐driven machine learning approach to construct a global daily ∼9 km resolution Probability of Fire (PoF) model operating at multiple lead times. …”
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