A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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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

“…The projected landscape diversity risk warning for 2025 mirrors the historical spatial data, with a notable reduction in local disparities and an overall decrease in the average value by 2.73%. …”
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Integrative Framework for Decoding Spatial and Temporal Drivers of Land Use Change in Malaysia: Strategic Insights for Sustainable Land Management by Guanqiong Ye, Kehao Chen, Yiqun Yang, Shanshan Liang, Wenjia Hu, Liuyue He

“…However, understanding spatial differentiation and conducting inter-regional comparisons of these drivers remains limited, particularly in regions like Malaysia, where complex interactions between human activities and natural conditions pose significant challenges. …”
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Spatial Association Network of Land-Use Carbon Emissions in Hubei Province: Network Characteristics, Carbon Balance Zoning, and Influencing Factors by Yong Huang, Zhong Wang, Heng Zhao, Di You, Wei Wang, Yanran Peng

“…This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. …”
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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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A new water temperature modeling approach to predict thermal habitat suitability for nonnative cichlids in Florida rivers by Alexandra M. Scott, Andrew K. Carlson

“…To understand how water temperature changes may affect the spatial distribution of these nonnative species, more effective water temperature prediction models are necessary. …”
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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

“…Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R²) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. …”
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Study on characteristics of heavy precipitation and model prediction correction under typical topography in Guizhou by Zhiyu LI, Xiaoling DU, Yulei ZHU, Qingxia XIE, Tao WEI, Wenyu ZHOU, Diannan LI

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Predicting vector distribution in Europe: at what sample size are species distribution models reliable? by Lianne Mitchel, Lianne Mitchel, Guy Hendrickx, Ewan T. MacLeod, Cedric Marsboom, Cedric Marsboom

“…IntroductionSpecies distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. …”
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Dynamic Prediction Method of 3D Spatial Information of Coal Mining Subsidence Water Area Integrated with Landsat Remote Sensing and Knothe Time Function by Hui Liu, Yu Li

“…Taking the 1031 working face of Wugou Coal Mine in Huaibei, Anhui, China, as the research subject, (1) a three-dimensional (3D) spatial information dynamic prediction method was proposed for high-water-level coal mining subsidence areas by combining the Knothe time function based on the probability integration method (PIM) and the principle of water balance. (2) The dynamic evolution law of the water accumulation area in the high-water-level coal mining subsidence area was studied. (3) The applicability of the dynamic prediction model of the water accumulation range in the high-water-level coal mining subsidence area was verified. …”
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Spatial distribution of archeological sites in the Kingdom of Saudi Arabia and the application of the theory of planned behavior to predict pro-environmental behavioral intention a... by Nada Sulaiman Mohammed Alali, Tarek Abdel Azim Ahmed, Eman Sarhan Shaker

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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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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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Housing, travel, and energy spatial-temporal simulation of Riyadh: Impacts of the New Murabba Project by Abdel Rahman Muhsen, John Abraham, Geraldine Fuenmayor, Paul McMillan, J. D. Hunt

“… The city of Riyadh in Saudi Arabia envisions rapid growth, from a 2020 population of 7.2 million to one reaching 15 million or more by 2030 (Alhefnawi et al., 2024). A spatial economic and transport model has been developed following well-established approaches to assist in forecasting the expansion of the city, particularly the spatial organization of the population, people's housing, economic activity and employment consumption, and the flows of goods and services on the transportation network. …”
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Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability by Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Rubson Natal Ribeiro Sibaldelli, Liang Sun, Renato Herrig Furlanetto, Sergio Luiz Gonçalves, Norman Neumaier, José Renato Bouças Farias

“…Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. …”
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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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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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