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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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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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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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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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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Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model by Yalei Yang, Hong Wang, Xiaobing Li, Tengfei Qu, Jingru Su, Dingsheng Luo, Yixiao He

“…A Potential-Connectedness-Resilience framework was used to assess landscape diversity risks from 2010 to 2020, with a Convolutional Neural Network combined with a Long Short-Term Memory (CNN-LSTM) model predicting future risks for 2025. Our findings indicate that landscape diversity in Inner Mongolia was favourable and stable condition during 2019–2021. …”
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A Spatial Long-Term Load Forecast Using a Multiple Delineated Machine Learning Approach by Terence Kibula Lukong, Derick Nganyu Tanyu, Yannick Nkongtchou, Thomas Tamo Tatietse, Detlef Schulz

“…Maintaining a balance between electricity generation and consumption is vital for ensuring grid stability and preventing disruptions. Spatial load forecasting (SLF) predicts geographical electricity demand, thereby aiding in power system planning. …”
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Burn Severity in Canada's Mountain National Parks: Patterns, Drivers, and Predictions by Weiwei Wang, Xianli Wang, Wanli Wu, Futao Guo, Jane Park, Guangyu Wang

“…The predicted burn severity potentials of the whole parks in 2002 and 2012 showed overall consistent spatial patterns, and lightning‐caused fires produced more high‐severity burn areas than prescribed fires. …”
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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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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Accurate prediction of greenhouse temperatures is essential for developing effective environmental control strategies, as the precision of minimum temperature data acquisition significantly impacts the reliability of predictive models. …”
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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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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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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. …”
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A Computational–Cognitive Model of Audio-Visual Attention in Dynamic Environments by Hamideh Yazdani, Alireza Bosaghzadeh, Reza Ebrahimpour, Fadi Dornaika

“…Inspired by cognitive studies, we propose a computational model that combines spatial, temporal, face (low-level and high-level visual cues), and auditory saliency to predict visual attention more effectively. …”
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Computational predictions of magnetic resonance acoustic radiation force imaging for breast cancer focused ultrasound therapy by Chloe K. Nelson, Michelle Kline, Allison Payne, Christopher R. Dillon

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