Based on the Prediction of Future Drought Evolution in Heilongjiang Province under the CMIP6 Model by LIU Tao, SI Zhenjiang, LIU Yan

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Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda by Jean Marie Vianney Ntamwiza, Hannibal Bwire

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Enhancing Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering by Ali Ardestani, Hao Yang, Saiedeh Razavi

“…Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. …”
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WoFSCast: A Machine Learning Model for Predicting Thunderstorms at Watch‐to‐Warning Scales by Montgomery L. Flora, Corey Potvin

“…Abstract Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. …”
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Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies by Hamed Bin Furkan, Nabila Ayman, Md. Jamal Uddin

“…This study addresses this gap by evaluating four established hybrid neural network models for predicting influenza outbreaks. These models were analyzed by employing time series data from eight different countries to challenge the models with imposed spatial difficulties, in a month-on-month structure. …”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. …”
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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

“…The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. …”
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Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid

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A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method by Zhuoran Yu, Yimeng Duan, Shen Zhang, Xin Liu, Kui Li

“…Dock-less bicycle-sharing programs have been widely accepted as an efficient mode to benefit health and reduce congestions. And modeling and prediction has always been a core proposition in the field of transportation. …”
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Assessment and Prediction of Coastal Ecological Resilience Based on the Pressure–State–Response (PSR) Model by Zhaoyi Wan, Chengyi Zhao, Jianting Zhu, Xiaofei Ma, Jiangzi Chen, Junhao Wang

“…In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China’s coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. …”
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Cellular automata models for simulation and prediction of urban land use change: Development and prospects by Baoling Gui, Anshuman Bhardwaj, Lydia Sam

“…Among them, Cellular Automata (CA) models have become key tools for predicting urban expansion, optimizing land-use planning, and supporting data-driven decision-making. …”
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Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer by Jun He, Zijian Cheng, Zijie Zhong, Lizhuo Liang, Jianhui Ye

“…This effectively captures potential interrelationships in wind power data from both temporal and spatial dimensions, followed by accurate and efficient predictions using the Informer model. …”
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19 Predicting daily PM2.5 in Mexico City: A hybrid spatiotemporal modeling approach by Mike He, Ellen Ren, Iván Gutiérrez-Avila, Itai Kloog

“…Objectives/Goals: In recent years, there has been growing interest in the development of air pollution prediction models, particularly in low- and middle-income countries that are disproportionately impacted by the effects of air pollution. …”
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. by Julia Ledien, Zulma M Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P Dobson, Susana B Adamo, María-Gloria Basáñez, Pierre Nouvellet

“…Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. …”
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Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus by J. M. Manel K. Herath, Hemalika T. K. Abeyasundara, W. A. Priyanka P. De Silva, Thilini C. Weeraratne, S. H. P. Parakrama Karunaratne

“…Another prediction model was developed using OVI and RH with one month lag period (R2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528∗ Lag 1 month RH; RMSE = 2.01). …”
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Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields by Qassim A. Talib Al-Shujairy, Suhad M. Al-Hedny, Mohammed A. Naser, Sadeq Muneer Shawkat, Ahmed Hatem Ali, Dinesh Panday

“…Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. …”
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An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin by Zhonghui Guo, Chang Feng, Liu Yang, Qing Liu

“…This integration of physics-based and data-driven modeling not only provides valuable insights into watershed ET modeling prediction and mechanistic understanding but also underscores the broader potential for application across global watersheds and related disciplines.…”
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Fast prediction of irradiation-induced cascade defects using denoising diffusion probabilistic model by Ruihao Liao, Ke Xu, Yifan Liu, Zibo Gao, Shuo Jin, Linyun Liang, Guang-Hong Lu

“…We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…First, tumor regions are delineated across MRI modalities and then modeled using a translation-invariant Markov-Gibbs random field (MGRF) with analytical parameter estimation to capture modality-specific spatial appearance patterns correlated with NAC response. …”
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Development of an AI model for DILI-level prediction using liver organoid brightfield images by Shiyi Tan, Yan Ding, Wei Wang, Jianhua Rao, Feng Cheng, Qiuyin Zhang, Tingting Xu, Tianmu Hu, Qinyi Hu, Ziliang Ye, Xiaopeng Yan, Xiaowei Wang, Mingyue Li, Peng Xie, Zaozao Chen, Geyu Liang, Yuepu Pu, Juan Zhang, Zhongze Gu

“…Here we show a drug-induced liver injury (DILI) level prediction model using HLO brightfield images (DILITracer) considering that DILI is the major causes of drug withdrawals. …”
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