Application of Proper Orthogonal Decomposition in Spatiotemporal Characterization and Reduced-Order Modeling of Rotor–Stator Interaction Flow Field by Yongkang Lin, Weijian Yang, Hu Wang, Fazhong Wang, Jie Hu, Jianyao Yao

“…This study derives the Toeplitz structure of the correlation matrix in proper orthogonal decomposition (POD) for strictly periodic flow fields and reveals that the POD spatial modes appear in pairs with a 90° spatial phase difference, which originates from the cosine and sine form of the eigenvectors of the Toeplitz matrix. …”
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Data sharing and GRA weight optimization for power prediction of distributed photovoltaic power plant considering missing NWP information by YANG Xiyun, YANG Yan, MENG Lingzhuochao, PENG Yan, WANG Chenxu

“…On this basis, this paper proposes a power prediction model for distributed photovoltaic power plant based on data sharing and grey relation analysis (GRA) weight optimization. …”
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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. …”
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Estimation of Forest Aboveground Biomass Using Sentinel-1/2 Synergized with Extrapolated Parameters from LiDAR Data and Analysis of Its Ecological Driving Factors by Xu Xu, Jingyu Yang, Shanze Qi, Yue Ma, Wei Liu, Luanxin Li, Xiaoqiang Lu, Yan Liu

“…Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. …”
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A new framework for landslide susceptibility mapping in contiguous impoverished areas using machine learning and catastrophe theory by Wei Zhou, Yingzhi Zhou, Shuneng Liang, Chengnian Zhang, Hongzhou Dai, Xiaofei Sun

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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

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Assessing conservation priorities for seahorses in Brazil reveals gaps in current protected areas by Anna Karolina Martins Borges, Vanessa M. Adams, Rômulo Romeu Nóbrega Alves, Tacyana Pereira Ribeiro Oliveira

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Using the SWAT model to characterise the water regime of soils in agrolandscapes by E. V. Shein, A. G. Bolotov, A. V. Dembovetskiy, D. Yu. Usenko, N. A. Kharkhardinov, Yu. I. Vernyuk

“…Numerical methods for representing the hydrological regimes of soils in the agricultural landscape are based on physically validated mathematical models of the soil water movement and spatial GIS information, which together allow to calculate, analyze and predict soil water regime, runoff in the scale of watersheds, substance transport in the soil profile, leaching processes, as well as the content of available soil moisture reserves in the agrolandscape structure – which is important for practice. …”
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Bayesian spatio-temporal modeling and prediction of malaria cases in Tanzania mainland (2016-2023): unveiling associations with climate and intervention factors by Lembris Laanyuni Njotto, Wilfred Senyoni, Ottmar Cronie, Anna-Sofie Stensgaard

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High-resolution global modeling of wheat’s water footprint using a machine learning ensemble approach by Murat Emeç, Abdullah Muratoğlu, Muhammed Sungur Demir

“…The study revealed distinct outcomes for different clustering methods, demonstrating the model's robustness across varying spatial scales. …”
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Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios by Arifur Rahman Rifath, Md Golam Muktadir, Mahmudul Hasan, Md Ashraful Islam

“…This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. …”
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Comprehensive propagation of errors for the prediction of woody biomass by Stephen H. Roxburgh, Keryn I. Paul

“…Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. …”
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Improving spatial resolution of Aqua MODIS and GCOM-C chlorophyll-a data for Cyprus coastal waters monitoring by Sofiia Drozd, Nataliia Kussul, Andrii Shelestov

“…Four images from spring-summer 2024 were selected for analysis, with Sentinel-3 spectral bands used as predictors and both multiple linear regression and random forest models applied. The results indicate that linear regression predicts higher coastal Chl-a values, while random forest smooths spatial gradients. …”
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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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ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model by Y. Liu, H. Huang, S.-C. Wang, T. Zhang, D. Xu, Y. Chen

“…Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. …”
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Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction by Leilei Kang, Guojing Hu, Hao Huang, Weike Lu, Lan Liu

“…In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. …”
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A prediction model of soil organic carbon into river and its driving mechanism in red soil region by Yanhu He, Yuyin Yang, Daoguo Xu, Zirui Wang

“…This study integrates the Soil and Water Assessment Tool (SWAT) for sediment yield simulation and a Soil Organic Carbon Content (SOCC) model to quantify SOCR at the basin scale. A Random Forest-based prediction model was developed to explore the spatial-temporal variability and driving mechanisms of SOCR in the Dongjiang River Basin (DRB), a representative red soil region in southern China. …”
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Modeling Porosity Distribution Strategies in PEM Water Electrolyzers: A Comparative Analytical and Numerical Study by Ali Bayat, Prodip K. Das, Suvash C. Saha

“…Despite this, conventional models often oversimplify key components, such as porous transport and catalyst layers, by assuming constant porosity and neglecting the spatial heterogeneity found in real electrodes. …”
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<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. …”
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