Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. …”
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Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and <i>K</i>-Means Clustering by Zhenzhu Meng, Yating Hu, Shunqiang Jiang, Sen Zheng, Jinxin Zhang, Zhenxia Yuan, Shaofeng Yao

“…Additionally, we employ a <i>k</i>-means clustering technique to account for both temporal and spatial variations in multi-point monitoring data, which improves the model’s ability to capture the relationships between monitoring points and increases prediction relevance. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…As such, the study proposes a combined prediction model. This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques.MethodsThe model uses the variational mode decomposition (VMD) method to break down the ship’s roll motion data into components at different scales. …”
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High-Resolution Daily XCH₄ Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data by Mohamad M. Awad, Saeid Homayouni

“…To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. …”
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Interpretation of Bayesian-optimized deep learning models for enhancing soil erosion susceptibility prediction and management: a case study of Eastern India by Meshel Alkahtani, Javed Mallick, Saeed Alqadhi, Md Nawaj Sarif, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

“…Addressing this issue requires advanced predictive models that can accurately identify areas at risk and inform soil conservation strategies. …”
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Prediction of Land Use Change and Carbon Storage in Lijiang River Basin Based on InVEST-PLUS Model and SSP-RCP Scenario by Jing Jing, Feili Wei, Hong Jiang, Zhantu Chen, Shuang Lv, Tengfang Li, Weiwei Li, Yi Tang

“…Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. …”
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Prediction of Complex Observed Shear Wave Splitting Patterns at Ryukyu Subduction Zone Using a Strong Intra‐Slab Anisotropy Model by Sharmila Appini, Jiaxuan Li, Hao Hu, Neala Creasy, Leon Thomsen, Joseph McNease, Yingcai Zheng

“…For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full‐wave seismic modeling, we showed that a dipping slab with ∼30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. …”
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Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK by Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie

“…FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. …”
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Deterministic Sea Wave Reconstruction and Prediction Based on Coherent S-Band Radar Using Condition Number Regularized Least Squares by Zhongqian Hu, Zezong Chen, Chen Zhao, Xi Chen

“…Coherent S-band radar is a remote sensing observation device with high spatial-temporal resolution and can be used to achieve deterministic sea wave reconstruction and prediction (DSWRP) technology. …”
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Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber by Bingqing Xiao, Songxi Yuan, Ákos Bede‐Fazekas, Jinxin Zhou, Xingyu Song, Qiang Lin, Lei Cui, Zhixin Zhang

“…We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert‐informed ensemble model that further accounted for distance to the IUCN expert range map. …”
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Thermal dose feedback control systems applied to magnetic nanoparticle hyperthermia by Yash Sharad Lad, Shreeniket Pawar, Nageshwar Arepally, Hayden Carlton, Constantinos Hadjipanayis, Robert Ivkov, Ma’Moun Abu-Ayyad, Anilchandra Attaluri

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From Field to Model: Determining EROSION 3D Model Parameters for the Emerging Biomass Plant <i>Silphium perfoliatum</i> L. to Predict Effects on Water Erosion Processes by Tobias Koch, Peter Aartsma, Detlef Deumlich, Peter Chifflard, Kerstin Panten

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Relative importance of temporal and location features in predicting smoking events by Han Yang, Hang Yu, Michael Kotlyar, Sheena R. Dufresne, Serguei V. S. Pakhomov

“…This study examined the predictive value of temporal and spatial features available from smartphones. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…First, the initial location prediction results are obtained through the meta-model. …”
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Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences by Rizwan Niaz, Mohammed M. A. Almazah, Xiang Zhang, Ijaz Hussain, Muhammad Faisal

“…Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. …”
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A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data by Md Sakhawat Hossain, Ravi Goyal, Natasha K. Martin, Victor DeGruttola, Mohammad Mihrab Chowdhury, Christopher McMahan, Lior Rennert

“…Methods To overcome this challenge, we propose a two-step approach that incorporates existing $$\:{R}_{t}$$ estimation procedures (EpiEstim, EpiFilter, EpiNow2) using data from geographic regions with sufficient data (step 1), into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predict $$\:{R}_{t}$$ in regions with sparse or missing data (step 2). …”
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Brain systems for probabilistic and dynamic prediction: computational specificity and integration. by Jill X O'Reilly, Saad Jbabdi, Matthew F S Rushworth, Timothy E J Behrens

“…We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. …”
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Satellite-Based Prediction of Water Turbidity Using Surface Reflectance and Field Spectral Data in a Dynamic Tropical Lake by Elsa Pereyra-Laguna, Valeria Ojeda-Castillo, Enrique J. Herrera-López, Jorge del Real-Olvera, Leonel Hernández-Mena, Ramiro Vallejo-Rodríguez, Jesús Díaz

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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…To address these limitations, we propose an adaptive spatiotemporal dynamic graph convolutional network (AST-DGCN) for traffic prediction. Under the encoder-decoder architecture, the proposed model leverages node embedding techniques to extract high-dimensional features, generating time-evolving adaptive graphs through self-attention mechanisms. …”
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