Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation. by Janis Keck, Caswell Barry, Christian F Doeller, Jürgen Jost

“…In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. …”
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From Domain Decomposition to Model Reduction for Large Nonlinear Structures by Leturcq, Bertrand, Le Tallec, Patrick

“…The numerical simulation of multiscale and multiphysics problems requires efficient tools for spatial localization and model reduction. A general strategy combining Domain Decomposition and Nonuniform Transformation Field Analysis (NTFA) is proposed herein for the simulation of nuclear fuel assemblies at the scale of a full nuclear reactor. …”
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Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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Modeling robustness tradeoffs in yeast cell polarization induced by spatial gradients. by Ching-Shan Chou, Qing Nie, Tau-Mu Yi

“…In this work, we investigated the tradeoffs among these performance objectives using a generic model that captures the basic spatial dynamics of polarization in yeast cells, which are small. …”
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An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle by Zhi Zhou, Xueling Wu, Bo Peng

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Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
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Event Prediction Using Spatial–Temporal Data for a Predictive Traffic Accident Approach Through Categorical Logic by Eleftheria Koutsaki, George Vardakis, Nikos Papadakis

“…Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL.…”
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Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response by Yuanyuan Wu, Youyang Shi, Zhanyang Luo, Xiqiu Zhou, Yonghao Chen, Xiaoyun Song, Sheng Liu

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Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches by Salman Selmy, Salah Abd El-Aziz, Ahmed El-Desoky, Moatez El-Sayed

“…The current study was undertaken in the Gharb El-Mawhoub area of Dakhla Oasis to determine, predict, map, and assess the spatial variation of physicochemical attributes. …”
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Modelling the Spatial Dependence of Multi‐Species Point Patterns by Chathuri L. Samarasekara, Ian Flint, Yan Wang

“…ABSTRACT The study of the spatial point patterns in ecology, such as the records of the observed locations of trees, shrubs, nests, burrows, or documented animal presence, relies on multivariate point process models. …”
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Digital agriculture drives carbon emission reduction in China by Yao Wenyan, Li Chenghan, Lin Fangzhou

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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

“…On the other hand, the SVR model demonstrated better predictive performance for Chl-a concentration retrieval using PlanetScope (PS) data (R2 = 0.71, RMSE = 8.15 μg/l, bias = 0.46). …”
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Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Gonghe Basin by Hong Jia, Siqi Yang, Lianyou Liu, Hang Li, Zeshi Li, Yixin Chen, Jifu Liu

“…Based on the land use data of the Gonghe Basin from 1990 to 2020, the InVEST model was applied to analyze the spatiotemporal changes in carbon storage, and the PLUS model was used to predict the changes in carbon storage under three different development scenarios in 2030. …”
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Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland by Zeinab Bandpey, Soroush Piri, Mehdi Shokouhian

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Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

“…Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. …”
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Urban Fire Spatial–Temporal Prediction Based on Multi-Source Data Fusion by Haiyu Xiang, Lizhi Wu, Zidong Guo, Shaoyun Ren

“…Temporal variables, such as past fire incidents and external influences like meteorological conditions, significantly impact fire risk, while spatial attributes, including regional characteristics and cross-regional interactions, further complicate predictive modeling. …”
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Vers une nouvelle classification des modèles d'évaluation et de prédiction de l'érosion hydrique by Aïcha Fadil, Farid El Wahidi

“…However, these models contrast considerably regarding to the studied phenomena, their nature and their complexity in the field, and to the implemented approach, through the variety and the quality of used data, the spatial and temporal scales of application and the information obtained as output, types and uncertainties.An attempt to classify water erosion assessment models (129 models are considered) is presented based on their most critical geospatial attributes for water erosion modeling. …”
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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…However, the task still faces challenges in modeling long-term dependencies, complex spatial interactions, and multi-scale feature fusion. …”
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