Spatially predicting ecosystem service patterns in boreal drained peatlands forests using multisource satellite data by Kaapro Keränen, Anwarul Islam Chowdhury, Parvez Rana

“…Incorporating auxiliary variables from seven-year-old LiDAR data improved model R2 value by 1–24 %. We successfully scaled ESs predictions to map spatial distributions across the study area, with high ESs value in closed-canopy areas. …”
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Predicted Spatial Patterns of Suitable Habitats for <i>Troides aeacus</i> Under Different Climate Scenarios by Biyu Liu, Xinqi Deng, Zhiqian Liu, Xinju Wei, Honghua Zhang, Danping Xu, Zhihang Zhuo

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OPTIMIZING ROAD SAFETY: THE ROLE OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN TRAFFIC ACCIDENT ANALYSIS AND PREDICTION by Mohammed Shukur Alfaras, Oğuz Karan, Sefer Kurnaz

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Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…The predictions reveal significant spatial variation in biomass density, reflecting region's diverse ecological zones &amp; land-use patterns. …”
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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…A predictive negative binomial regression (NBR) model was created, and the presence of spatial autocorrelation was examined to account for spatial dependencies in the outcome variable. …”
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Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration by Junjie Jiang, Lingxia Feng, Junguo Hu, Chao Zhu

“…Additionally, we found significant biases in the annual Rs calculated by area weighting based on climate and ecosystem classifications because these factors characterise spatial heterogeneity differently. Such dynamics should be considered when modelling global Rs and analysing the results because they can help improve the estimation accuracy of global Rs prediction models.…”
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Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland by Zeinab Bandpey, Soroush Piri, Mehdi Shokouhian

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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…The emergence of machine learning (ML) and, more specifically, deep learning (DL) has introduced new techniques that significantly enhance prediction accuracy. ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. …”
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Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak by Alvin Garcia, Sydney Liu, Xiaodi Du, Max Curie, Azarakhsh Jalalvand, Peter Steiner, Egemen Kolemen

“…This improvement highlights the model’s enhanced predictive accuracy for measured images from the validation set. …”
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Actual and Predictive Transport Modeling of Fluoride Contamination of the Sfax-Agareb Coastal Aquifer in the Mediterranean Basin by Samira Melki, Amina Mabrouk El Asmi, Mohamadou Ould Baba Sy, Moncef Gueddari

“…This study, using numerical modeling, gives new insights for short- and medium-term prediction of eventual fluoride concentrations in the saturated zone of the Sfax-Agareb aquifer.…”
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Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

“…Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. …”
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Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments by Megha Sharma, Shailendra Goel, Ani A. Elias

“…The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. …”
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A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling by Melissande Machefer, Michele Ronco, Anne-Claire Thomas, Michael Assouline, Melanie Rabier, Christina Corbane, Felix Rembold

“…The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.…”
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Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma by Daniel A. Ruiz-Torres, Michael E. Bryan, Shun Hirayama, Ross D. Merkin, Evelyn Luciani, Thomas J. Roberts, Manisha Patel, Jong C. Park, Lori J. Wirth, Peter M. Sadow, Moshe Sade-Feldman, Shannon L. Stott, Daniel L. Faden

“…Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. …”
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Individual mobility prediction by considering current traveling features and historical activity chain by Xiaotong Zhang, Zhipeng Gui, Yuhang Liu, Dehua Peng, Qianxi Lan, Zhangxiao Shen, Huan Chen, Yuhui Zuo, Yao Yao, Huayi Wu, Kai Li, Kun Qin

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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma by Kai Lei, Kai Lei, Yutong Zhao, Shumin Li, Jiawei Liu, Wenhao Chen, Caihong Zhou, Yi Zhang, Jinmei Tan, Jian Wu, Qi Zhou, Qi Zhou, Jiehui Tan

“…This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences.MethodsWe analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. …”
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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.…”
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Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

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