A statistical framework for modelling migration corridors by Tristan A. Nuñez, Mark A. Hurley, Tabitha A. Graves, Anna C. Ortega, Hall Sawyer, Julien Fattebert, Jerod A. Merkle, Matthew J. Kauffman

“…We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. …”
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A Secure and Efficient Framework for Multimodal Prediction Tasks in Cloud Computing with Sliding-Window Attention Mechanisms by Weiyuan Cui, Qianye Lin, Jiaqi Shi, Xingyu Zhou, Zeyue Li, Haoyuan Zhan, Yihan Qin, Chunli Lv

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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

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Assessing the ecological risk and its driving forces on Islands using the Pressure-State-Response model by Meina Xu, Xiaochen Huang, Jiajun Li, Shaojun Qi, Ye Zhang, Xiaojie Zhang

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Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression by Leilei Li, Mingzhen Jia, Chong Xu, Yingying Tian, Siyuan Ma, Jintao Yang

“…In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. …”
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User Trajectory Prediction in Cellular Networks Using Multi-Step LSTM Approaches: Case Study and Performance Evaluation by Iskandar, Hajiar Yuliana, Hendrawan, Adriel Timoteo, Fabian Rafinanda Benyamin, Naufal Bhanu Anargyarahman

“…While LSTM excels in capturing sequential temporal patterns, Transformer introduces multi-head attention mechanisms to model complex spatial and temporal dependencies, filling a significant research gap in trajectory prediction. …”
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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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Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains by Shiheng Duan, Paul Ullrich, Mark Risser, Alan Rhoades

“…To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. …”
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Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb, Margaret Bell

“…Compared to the best-performing traditional model (Linear Regression, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.3520</mn></mrow></semantics></math></inline-formula>), HCB-Net improved predictive accuracy by 13.5% in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>, and outperformed other deep learning models such as LSTM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mo>−</mo><mn>0.3756</mn></mrow></semantics></math></inline-formula>) and GRU (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mo>−</mo><mn>0.6276</mn></mrow></semantics></math></inline-formula>), which failed to capture spatial patterns effectively. …”
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SHAP-enhanced interpretive MGTWR-CNN-BILSTM-AM framework for predicting surface subsidence: a case study of Shanghai municipality by Long Wen-Jiang, Yu Xue-Xiang, Zhu Ming-Fei, Xue Li, Zhang Guang-Hui, Wang Lin-Lin

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MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq by Nabaz R. Khwarahm

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Mechanical prediction method of strata movement and surface subsidence in backfill-strip mining by Xiaojun Zhu, Feng Zha, Guangli Guo, Qingliang Chang, Hui Liu, Xiaoyu Yang

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Temporal-Spatial Redundancy Reduction in Video Sequences: A Motion-Based Entropy-Driven Attention Approach by Ye Yuan, Baolei Wu, Zifan Mo, Weiye Liu, Ji Hong, Zongdao Li, Jian Liu, Na Liu

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Prediction of the Morphological Characteristics of Asymmetric Thaw Plate of Qinghai–Tibet Highway Using Remote Sensing and Large-Scale Geological Survey Data by Jianbin Hao, Zhenyang Zhao, Jianbing Chen, Zhiyun Liu, Fuqing Cui, Xiaona Liu, Wenting Lu, Jine Liu

“…Through integrating remote sensing data and large-scale geological survey results with an earth–atmosphere coupled numerical model and a random forest (RF) prediction framework, we assessed the spatial distribution of thaw asymmetry along the permafrost section of the QTH. …”
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A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity by Guangwen Peng, Yingbing Liu, Cong Xiao, Wenying Du, Changjiang Xiao

“…AGCRUs dynamically construct topological relationships via graph convolution to model spatial variations, while GRUs capture temporal dependencies. …”
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Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling. by Shuoying Ning, Jiufeng Wei, Jinian Feng

“…Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potentially suitable climactic areas but also to predict where the pest is likely to spread in the future so that appropriate monitoring and management programs can be developed. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…Our approach leverages cutting-edge ML techniques, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM), to develop highly accurate predictive models. The performance of these models was rigorously evaluated using comprehensive statistical metrics, such as Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), precision, recall, and overall accuracy (OA). …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

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Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy by Mauro Francesco Pio Maiorano, Gennaro Cormio, Vera Loizzi, Brigida Anna Maiorano

“…Pooled AUCs indicated strong predictive performance for genomics-based (0.78), radiomics-based (0.88), and immunotherapy-based (0.77) models. …”
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