Corrigendum: 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

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TGN: A Temporal Graph Network for Physics Prediction by Miaocong Yue, Huayong Liu, Xinghua Chang, Laiping Zhang, Tianyu Li

“…Long-term prediction of physical systems on irregular unstructured meshes is extremely challenging due to the spatial complexityof meshes and the dynamic changes over time; namely, spatial dependence and temporal dependence. …”
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Drone-assisted climate-smart agriculture (DACSA): A spatially-based outcome prediction model as an initial approach to track yield changes in shallot planting areas

“…Machine learning algorithms were employed to make predictions, and the yield projections were integrated into spatial maps. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…The dynamic learning of spatial correlations, combined with the integration of road characteristics and contextual variables, significantly enhances the accuracy of traffic accident predictions. …”
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Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine by Penghai Zhang, Yang Li, Xin Dong, Tianhong Yang, Honglei Liu

“…The displacement time series were decomposed using Variational Mode Decomposition (VMD) into trend and periodic components, for which Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models were respectively developed. The results indicate that (1) DBSCAN effectively detects clusters characterized by high average cumulative displacement and broad spatial distribution, while filtering out isolated outliers. (2) The trend component prediction achieved a coefficient of determination (R²) of 0.99755, while the periodic component prediction yielded a root mean square error (RMSE) of just 0.0978 mm. …”
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Spectroscopic analysis (UV-VIS-NIR) for predictive modeling of macro and micronutrients in grapevine leaves by J.I. Manzano, M. Rodríguez-Febereiro, M. Fandiño, M. Vilanova, J.J. Cancela

“…The ultraviolet (UV) range played a minor role, highlighting the predominant importance of the VIS-NIR regions in spectroscopic analyses.Finally, the results support the potential of this technique for swiftly and non-invasively predicting both macro and micronutrient levels in grapevine plants, and facilitate the fertilization planning using variety-specific reference levels, or precision viticulture adapted to site-specific demands, including spatial intra-plot variability.…”
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Single-nucleotide polymorphism analysis accurately predicts multiple impairments in hippocampal activity and memory performance in a murine model of idiopathic autism by Isabel Barón-Mendoza, Luis A. Márquez, Aliesha González-Arenas, Jessica Guzmán-Condado, Vladimir A. Martínez-Rojas, Johaly Anguiano-Buenfil, Montserrat Mejía-Hernández, Jorge Luis Almazán, Leonor Pérez-Martínez, Gustavo Pedraza-Alva, Emilio J. Galván, Angélica Zepeda

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Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor by Ran Yang, Ran Yang, Lulu Xie, Rui Wang, Yi Li, Yifei Lu, Baihui Liu, Shuyang Dai, Shan Zheng, Shan Zheng, Kuiran Dong, Kuiran Dong, Rui Dong, Rui Dong

“…A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. …”
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A New Prediction Model of Dam Deformation and Successful Application by Shuangping Li, Bin Zhang, Meng Yang, Senlin Li, Zuqiang Liu

“…In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. …”
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A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals by Liam Jordan Berrisford, Hugo Barbosa, Ronaldo Menezes

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Connectome-based prediction of functional impairment in experimental stroke models. by Oliver Schmitt, Peter Eipert, Yonggang Wang, Atsushi Kanoke, Gratianne Rabiller, Jialing Liu

“…Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. …”
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Liner Wear Prediction Using Bayesian Regression Models and Clustering by Jacob Van Den Broek, Melinda Hodkiewicz, Adriano Polpo

“…Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…With the goal of addressing the difficulty of rapidly identifying the source of fire in commercial buildings, this study builds a numerical fire model based on the fire dynamics simulator (FDS) and combines it with a hybrid model to predict the location of a fire source. …”
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Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory by Yaoqi Peng, Yudong Zheng, Zengwei Zheng, Yong He

“…By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R², prediction speed, training time, and model size. …”
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Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…A comparison of spatiotemporal prediction models' estimates of groundwater recharge in Morocco revealed AdaBoost and RF were the more accurate methods for temporal and spatial prediction, with RMSE values of 10.9712 mm/month and 5.0089 mm/month, respectively. …”
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Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration by Divas Karimanzira

“…In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. …”
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Traffic flow prediction based on spatiotemporal encoder-decoder model. by Yuanming Ding, Wei Zhao, Lin Song, Chen Jiang, Yunrui Tao

“…To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. …”
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Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China by Haizhi Luo, Yiwen Zhang, Xinyu Gao, Zhengguang Liu, Xiangzhao Meng, Xiaohu Yang

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Complex multivariate model predictions for coral diversity with climatic change by Tim R. McClanahan, Maxwell K. Azali, Nyawira A. Muthiga, Sean N. Porter, Michael H. Schleyer, Mireille M. M. Guillaume

“…Projecting changes using Coupled Model Intercomparison Project (CMIP) 2050 Representative Concentration Pathways (RCP2.6 and 8.5) water temperature predictions indicated high local variability and fewer negative effects than predictions made by coarse scale threshold and niche models. …”
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Application of Radionuclides Migration Software(MNS) in Radionuclides Diffusion Modeling by Yao LI, Rui-hao LI, Duo ZHOU, Xi CHEN, Yu-wei XU, Bo WANG

“…Pre-processing involves the construction of spatial models and parameter assignment, such as porosity, mineral density, radionuclide diffusion and migration parameters, and water flow velocity. …”
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