Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions by Vincenzo Giannico, Simone Pietro Garofalo, Luca Brillante, Pietro Sciusco, Mario Elia, Giuseppe Lopriore, Salvatore Camposeo, Raffaele Lafortezza, Giovanni Sanesi, Gaetano Alessandro Vivaldi

“…In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. …”
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Modelling the Spatial Dependence of Multi‐Species Point Patterns by Chathuri L. Samarasekara, Ian Flint, Yan Wang

“…Using synthetic and real datasets, we assessed both models based on their predictive accuracy of the empirical K function. …”
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Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia by Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono

“…Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction and risk mapping. …”
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A novel ST-iTransformer model for spatio-temporal ambient air pollution forecasting by Rui Zhang, Norhashidah Awang

“…Additionally, ablation experiments confirm that the spatio-temporal embedding, the inclusion of spatial data, and the addition of meteorological data all improve the prediction accuracy of the model. …”
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Using extreme gradient boosting for predictive urban expansion analysis in Rustenburg, South Africa from 2000 to 2030 by Paidamwoyo Mhangara, Eskinder Gidey, Bruce Steadman Mayise

“…This study modeled the spatial extent of urban growth in Rustenburg from 1994 to 2022 using Extreme Gradient Boosting (XGB) and predicted future urban expansion from 2022 to 2030 through the Cellular Automata Simulation in the MOLUSCE plugin. …”
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Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin by Idrissou Ahoudou, Nicodeme V. Fassinou Hotegni, Charlotte O. A. Adjé, Tania L. I. Akponikpè, Dêêdi E. O. Sogbohossou, Nadia Fanou Fogny, Françoise Assogba Komlan, Ismail Moumouni-Moussa, Enoch G. Achigan-Dako

“…The GWLR model excels in elucidating the spatial nuances of diverse factors, offering a promising avenue for more reliable predictions for OFSP adoption.…”
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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…Therefore, it is crucial to develop automated and efficient methods for predicting therapeutic outcomes.MethodsWe have developed a predictive model for the surgical efficacy in ME patients based on deep learning and optical coherence tomography (OCT) imaging, aimed at predicting the treatment outcomes at different time points. …”
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<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

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Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data by Yifan Liu, Huabiao Jin, Xiangguo Yang, Telu Tang, Qijia Song, Yuelin Chen, Lin Liu, Shoude Jiang

“…Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. …”
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Potential Distribution Prediction of Terminalia chebula Retz. in China Under Current and Future Climate Scenarios by Zhang‐Hong Dong, Hua Jiang, Wei Zhang, Jianhua Wu, Yanping Yang, Taoming Yang, Jiangping Zhao, Cunzhen Luo, Xiaoxia Yang, Guilin Li

“…Utilizing GIS and MaxEnt model, we predicted the spatial distribution of Terminalia chebula Retz. in China for the current and for the future (2050s and 2070s) under the RCP4.5 and RCP8.5 representative concentration pathways. …”
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Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber) by H. A. Cheriyanda Raveendra, L. Picco, F. Pirotti, F. Pirotti

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Study and Prediction of Land Use/Cover Change in Small and Medium-Sized River Basins in Typical Urbanization Areas by LI Liujie, LIU Bingjun, WEI Qiuying

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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. …”
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Application of deep learning in cloud cover prediction using geostationary satellite images by Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn

“…We explore the effectiveness of advanced deep learning techniques – specifically 3D Convolutional Neural Networks, Long Short-Term Memory networks, and Convolutional Long Short-Term Memory (ConvLSTM) – using GK2A cloud detection data, which provides updates every 10 minutes at 2 km spatial resolution. Our model utilizes training sequences of four past hourly images to predict cloud cover up to 4 hours ahead. …”
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A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Western North Pacific Monsoon Gyres: A New Identification Approach, Interannual Variability, and Seasonal Prediction by Yuqi Zang, Haikun Zhao, Philip J. Klotzbach, Chao Wang, Liguang Wu

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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

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Prediction, Prevention, and Control of “Overall–Local” Coal Burst of Isolated Working Faces Prior to Mining by Ming Zhang, Shiji Yang

“…Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. …”
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LinU-Mamba: Visual Mamba U-Net with Linear Attention to Predict Wildfire Spread by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. …”
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