Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory. by Qi Dai, Xiao-Yan Liu, Fang-Yi Sun, Fang-Rong Ren

“…The study carries out regression analysis and a long-short-term memory model (LSTM) to respectively filter out the factors and predict TCEI. …”
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Facial muscle mapping and expression prediction using a conformal surface-electromyography platform by Hila Man, Paul F. Funk, Dvir Ben-Dov, Chen Bar-Haim, Bara Levit, Orlando Guntinas-Lichius, Yael Hanein

“…Using this foundation, we demonstrated a deep-learning model to predict facial expressions. This approach enables precise, participant-specific monitoring with applications in medical rehabilitation and psychological research.…”
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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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Improving rice yield prediction with multi-modal UAV data: hyperspectral, thermal, and LiDAR integration by Shaofeng Tan, Jie Pei, Yaopeng Zou, Huajun Fang, Tianxing Wang, Jianxi Huang

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Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer by Mary Lee, George T Chen, Eric Puttock, Kehui Wang, Robert A Edwards, Marian L Waterman, John Lowengrub

“…Partial interference with Wnt alters the size and intensity of the spotted pattern in tumors and in the model. The model predicts that Wnt inhibition should trigger an increase in proteins that enhance the range of Wnt ligand diffusion. …”
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A Multimodal Deep Learning Framework for Accurate Biomass and Carbon Sequestration Estimation from UAV Imagery by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Xusinov Ibragim Ismailovich, Young Im Cho

“…A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. …”
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Predictive Assessment of Forest Fire Risk in the Hindu Kush Himalaya (HKH) Region Using HIWAT Data Integration by Sunil Thapa, Tek Maraseni, Hari Krishna Dhonju, Kiran Shakya, Bikram Shakya, Armando Apan, Bikram Banerjee

“…The system’s integration of satellite data and high-resolution forecasts improves the spatial and temporal accuracy of fire danger predictions. …”
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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…The relative error obtained by comparison of observed map versus simulated map for patch density, related circumscribing circle, and for effective mesh size metrics was the highest. The model was able to predict shape complexity, fragmentation, compactness and spatial heterogeneity, and area of forest class with high consistency. …”
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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

“…Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. …”
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An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining by Kaiwen Lixia, Mingyue Lu, Yifei Lu, Hui Liu, Ping Li

“…The joint method bridges the gap in deep learning models’ ability to process spatial information and the shortcomings of spatiotemporal similarity feature mining models in predicting future data. …”
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Prediction of drought-flood prone zones in inland mountainous regions under climate change with assessment and enhancement strategies for disaster resilience in high-standard farml... by Yongheng Shen, Qingxia Guo, Zhenghao Liu, Yanli Shen, Yikun Jia, Yuehan Wei

“…The overall findings indicate that: (1) Precipitation (Pr) and the Standardized Precipitation-Evapotranspiration Index (SPEI) have increased in recent years, with Pr expected to continue rising until 2035. (2) The integration of historical data with the predictions from the PSO-LSTM-GAT model reveals significant spatial overlap between historical and future disaster-prone areas and intensive cropland, especially in the central region. (3) Compared to single models, the PSO-LSTM-GAT model demonstrates significantly improved performance and precision in predicting drought- and flood-prone areas. (4) Through the FDRA integrated adjustment mechanism, 6.6668 km² of unsuitable land was identified, and 6.7349 km² of high-quality land was selected as the proposed site for the next round of HSF projects. …”
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Pharmacophore-Aware Dual-View Learning With Bidirectional Cross-Attention for Drug-Drug Interaction Prediction by Wenxiao Zhang, Seong Yoon Shin, Hailiang Tang

“…Existing methods often rely on single-view molecular representations, limiting their ability to capture the complex structural and spatial properties of drugs. In this study, we propose a novel pharmacophore-aware dual-view learning framework (PharmaDual) that integrates both 2D and 3D representations of pharmacophores for enhanced DDI prediction. …”
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Sensitivity Analysis of the WRF Model to Simulate Precipitation in the Metropolitan Area of the Valley of Mexico for the Period June-September 2019 by Indalecio Mendoza Uribe, Víctor Kevin Contreras Tereza, Pamela Iskra Mejía Estrada, Olivia Rodríguez López

“…The results of the model were compared with the observation records, considering five thresholds of rainfall. …”
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Integration of fuzzy logic and geographic weighted regression modeling for enhanced groundwater potential mapping using remote sensing and GIS by Junguang Gao, Amnah A. Alasgah, Imran Ahmad, Faten Nahas, Mithas Ahmad Dar, Martina Zelenakova, Milashu Sisay, Getanew Sewnet Zewdu

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A Prior Knowledge-Enhanced Deep Learning Framework for Improved Thermospheric Mass Density Prediction by Ling Li, Changyong He, Dunyong Zheng, Shaoning Li, Dong Zhao

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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable... by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

“…Results: 1) From 2014 to 2021, the annual catch showed an overall increasing trend and peaked at 220,009.063 tons in 2021; the total monthly catch increased and then decreased, with a peak of 76, 033.4944 tons (July), and the catch was mainly concentrated in the regions of 39.5°-43°N and 146.75°-155.75°E; 2) Catboost model predicted better than LightGBM and XGBoost models, with the highest values of accuracy and F1-score, 73.8% and 75.31%, respectively; 3) the overall importance ranking of the model’s built-in method differed significantly from that in the SHAP method, and the overall importance ranking of the spatial variables in the SHAP method increased. …”
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Expansion of Impervious Surface Area in Pekanbaru (1990–2018) and Predictions for 2038 Using Big Data by Wirawan Bayu Andrianto, Siregar Yusni Ikhwan, Sukendi Sukendi, Mulyadi Mulyadi

“…Through the polynomial regression modeling process with python, from the 1990-2018 data we can predict the area of built-up land in Pekanbaru City in 2038 is around 27985.139 hectares with r2 = 0.97. …”
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STGATN: A novel spatiotemporal graph attention network for predicting pollutant concentrations at multiple stations. by Huazhen Xu, Wei Song, Lanmei Qian, Xiangxiang Mei, Guojian Zou

“…In pollution prediction tasks, three key factors are essential: (1) dynamic dependencies among global monitoring stations should be considered in spatial feature extraction due to the diffusion properties of air pollutants; (2) precise temporal correlation modeling is critical because pollutant concentrations change dynamically and periodically; (3) it is vital to avoid propagation of long-term prediction errors across spatiotemporal dimensions. …”
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Spatial risk modelling of highly pathogenic avian influenza in France: Fattening duck farm activity matters. by Jean Artois, Timothée Vergne, Lisa Fourtune, Simon Dellicour, Axelle Scoizec, Sophie Le Bouquin, Jean-Luc Guérin, Mathilde C Paul, Claire Guinat

“…In this study, we present a comprehensive analysis of the key spatial risk factors and predictive risk maps for HPAI infection in France, with a focus on the 2016-17 and 2020-21 epidemic waves. …”
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Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava by Yevhen F. Suprunenko, Christopher A. Gilligan

“…However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. …”
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