Predicting changes in land use and land cover using remote sensing and land change modeler by Brijmohan Bairwa, Rashmi Sharma, Arnab Kundu, Saad Sh. Sammen, Fahad Alshehri, Chaitanya Baliram Pande, Chaitanya Baliram Pande, Zoltan Orban, Ali Salem, Ali Salem

“…The integration of geo-spatial and remote sensing technologies is pivotal in comprehending these dynamics and formulating strategies for future natural resource management. …”
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Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model by Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail

“…The feature importance score indicated river water level and rainfall contributes most to the model. Conclusions This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. …”
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The spatial resolution of epidemic peaks. by Harriet L Mills, Steven Riley

“…Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. …”
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GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer by Oumeima Thaalbi, Moulay A. Akhloufi

“…<i>Background:</i> Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. …”
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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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Modeling and predicting land use and land cover changes using remote sensing in tropical coastal ecosystems of southern Peru by Ellen Delgado, Elgar Barboza, Nilton B. Rojas-Briceño, Alexander Cotrina-Sanchez, Armando Valdés-Velásquez, Rocío López de la Lama, Camila Llerena-Cayo, Santiago de la Puente

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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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Active travel modelling: a methodological approach to networks for walking and cycling commuting analysis by Ivann Schlosser, Valentina Marin, Richard Milton, Elsa Arcaute, Michael Batty

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

“…However, due to strong nonlinear characteristics and spatiotemporal correlations of the traffic within the network, traffic flow prediction has been a challenging task. In order to capture the spatiotemporal correlation, and improve the traditional methods of using predefined adjacency matrices that cannot effectively characterise the dynamic correlation of traffic flow, a GE-GRU-KNN model for predicting the road traffic flow is proposed. …”
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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

“…Experimental results show that Conv-LSTM is better than the benchmark models in capturing spatial and temporal correlation.…”
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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

“…As chip usage frequency rises, device degradation introduces seasonal and trend patterns in droplet motion time data, complicating predictive modeling. This paper first employs the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to analyze the droplet motion time data in digital microfluidic systems. …”
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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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Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning by Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl

“…This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m3) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). …”
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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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Towards biologically realistic estimates of home range and spatial exposure for colonial animals by Holly I. Niven, Jana W. E. Jeglinski, Geert Aarts, Ewan D. Wakefield, Jason Matthiopoulos

“…Accurate home range (HR) estimation is therefore fundamental for spatial risk assessment. HRs are shaped by complex interactions between landscape permeability to movement and spatial resource competition between and within colonies, which are challenging to implement with density estimation methods (e.g. kernel smoothing) or species distribution models. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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