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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Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin by S. Wongchuig, R. Paiva, V. Siqueira, F. Papa, A. Fleischmann, S. Biancamaria, A. Paris, M. Parrens, A. Al Bitar

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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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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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Exploring urban land surface temperature with geospatial and regression modelling techniques in Uttarakhand using SVM, OLS and GWR models by Waiza Khalid, Syed Kausar Shamim, Ateeque Ahmad

“…Spatial autocorrelation analysis, utilizing Moran’s I, exhibited a decrease from 0.606 (OLS) to 0.02 (GWR), This reduction indicates that GWR effectively captures spatial non-stationarity, minimizing residual autocorrelation by modeling local relationships between LST and its predictors that often remain in global models like OLS, thereby demonstrating its advantages in heterogeneous regions. …”
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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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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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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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