Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach by Leonid Schwenke, Stefan Bloemheuvel, Martin Atzmueller

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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.…”
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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

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Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 by Chaibo Jose Armando, Joacim Rocklöv, Mohsin Sidat, Yesim Tozan, Alberto Francisco Mavume, Maquins Odhiambo Sewe

“…This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). …”
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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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DBSCAN-PCA-INFORMER-Based Droplet Motion Time Prediction Model for Digital Microfluidic Systems by Zhijie Luo, Bin Zhao, Wenjin Liu, Jianhua Zheng, Wenwen Chen

“…Subsequently, principal component analysis (PCA) is applied for dimensionality reduction on the clustered data. Using the INFORMER model, we predict changes in droplet motion time and conduct correlation analysis, comparing results with traditional long short-term memory (LSTM), frequency-enhanced decomposed transformer (FEDformer), inverted transformer (iTransformer), INFORMER, and DBSCAN-INFORMER prediction models. …”
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Phase field modeling for fracture prediction in goat tibia using an open-source quantitative computer tomography based finite element framework by Debangshu Paul, Zachariah Arwood, Pierre-Yves Mulon, Dayakar Penumadu, Timothy Truster

“…While predicting mechanical responses under various stress scenarios is of significant interest in the field of orthopedic research, finite element (FE) modeling studies specifically focusing on the tibia remain notably limited. …”
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Parameter-Efficient Vehicle Trajectory Prediction Based on Attention-Enhanced Liquid Structural Neural Model by Ruochen Wang, Yue Chen, Renkai Ding, Qing Ye

“…In this paper, we propose a parameter-efficient trajectory prediction model that integrates Liquid Time-Constant (LTC) networks with attention mechanisms, termed the Attn-LTC model. …”
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Evaluating the Accuracy of Land-Use Change Models for Predicting Vegetation Loss Across Brazilian Biomes by Macleidi Varnier, Eliseu José Weber

“…Land-use change models are used to predict future land-use scenarios. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…However, existing models often overlook the spatial deflection correlations among monitoring points. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…Relatively, RF, GLMET, and KNN performed better, compared to other models. The terrain attributes were significantly more successful as to the spatial predictions of the elements contained in laterites than were the remote sensing spectral indices, likely due to the fact that the underlying spatial structures of the two formations (laterite and talus) occur at different elevations.…”
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Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: Implications for polic... by Menkeoma Laura Okoli, Samuel Alao, Somtochukwu Ojukwu, Nnadozie C Emechebe, Asuelimen Ikhuoria, Kevin E Kip

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Real-Time Adaptive Traffic Flow Prediction Based on a GE-GRU-KNN Model by Xiangyu YI, Hongmei ZHOU, Shaopeng ZHONG

“…The results show that compared with traditional methods, the prediction error of this method is reduced by 1.08%–14.71%, indicating that the hybrid GE-GRU-KNN model exhibits good performance.…”
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A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction by Wei Chen, Zongping Li, Can Liu, Yi Ai

“…As a spatiotemporal sequence, the input and prediction targets are both spatiotemporal three-dimensional tensors in the end-to-end training model. …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. …”
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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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Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling by Hyunhye Song, Kiyeol Kim, Jihun Shin, Gitae Roh, Changsu Shim

“…Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. …”
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3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models by Hichem Horra, Ahmed Hadjadj, Elfakeur Abidi Saad, Khalil Moulay Brahim

“…This study advances petroleum industry knowledge by integrating deep learning and geostatistical methods to overcome rock strength prediction limitations in nonreservoir formations. The novel 3D model enhances the prediction range and spatial resolution, addresses data gaps and enables better decision-making for areas with limited wireline data.…”
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From Prediction to Explanation: Using Explainable AI to Understand Satellite-Based Riot Forecasting Models by Scott Warnke, Daniel Runfola

“…This study investigates the application of explainable AI (XAI) techniques to understand the deep learning models used for predicting urban conflict from satellite imagery. …”
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Predicting ecotopes from hydrodynamic model data: Towards an ecological assessment of nature-based solutions by Soesja Brunink, Gijs G. Hendrickx

“…Quantifying the current ecological state and future ecological shifts faces challenges, including variable dependencies, spatial-temporal disparities, and the limitations in available information. …”
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