Daily soil moisture prediction during winter wheat growth season using an SCSSA-CNN-BiLSTM model by CUI Song, WU Jin, ZHANG Naifeng, LIU Meng, HU Yongsheng, HE Yanan, GU Yue, LONG Xinya, WANG Zhenlong

“…This paper proposes a new method to predict soil moisture changes.【Method】The hybrid deep learning model, SCSSA-CNN-BiLSTM, was integrated with Sine Cosine Cauchy Sparrow Search Algorithm (SCSSA) for hyperparameter optimization. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…In this article, uneven-aged Chinese fir (<italic>Cunninghamia lanceolata</italic>) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. …”
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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. …”
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A Fluid Flow‐Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage by Zhen Qin, Yingxiang Liu, Fangning Zheng, Behnam Jafarpour

“…Abstract Prediction of the spatial‐temporal dynamics of the fluid flow in complex subsurface systems, such as geologic CO2 storage, is typically performed using advanced numerical simulation methods that solve the underlying governing physical equations. …”
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Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber) by H. A. Cheriyanda Raveendra, L. Picco, F. Pirotti, F. Pirotti

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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

“…The assessment of soil conservation measures requires calibrated soil erosion models that spatially identify soil erosion processes. …”
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Ultrasonic Experimental Evaluation of the Numerical Model of the Internal Fluid Flow in the Kidney Cooling Jacket by Barbara Gambin, Ilona Korczak-Cegielska, Wojciech Secomski, Eleonora Kruglenko, Andrzej Nowicki

“…By comparing the numerical results with experimental data, the simplified 2D model is shown to be accurate enough to predict the flow distribution of the internal fluid velocity field within the KCJ. …”
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Multi-view fusion of diffusion MRI microstructural models: a preterm birth study by Rosella Trò, Monica Roascio, Domenico Tortora, Mariasavina Severino, Andrea Rossi, Andrea Rossi, Eleftherios Garyfallidis, Gabriele Arnulfo, Gabriele Arnulfo, Marco Massimo Fato, Shreyas Fadnavis

“…Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. …”
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The precipitous decline of a gray fox population by Max R. Larreur, Clayton K. Nielsen, Damon B. Lesmeister, Guillaume Bastille-Rousseau

“…We then developed three predictive occupancy models that allowed comparison of gray fox spatial patterns and occupancy estimates over time. …”
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Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020 by Boyang Wang, Jianhua Si, Bing Jia, Xiaohui He, Dongmeng Zhou, Xinglin Zhu, Zijin Liu, Boniface Ndayambaza, Xue Bai

“…The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. …”
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A Spatial–Temporal Analysis and Multi-Scenario Projections of Carbon Sequestration in Sea Islands: A Case Study of Pingtan Island by Siyu Chen, Ming Xu, Heshan Lin, Fei Tang, Jinyan Xu, Yikang Gao, Yunling Zhuang, Yong Chen

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Bayesian feedback in the framework of ecological sciences by Mario Figueira, Xavier Barber, David Conesa, Antonio López-Quílez, Joaquín Martínez-Minaya, Iosu Paradinas, Maria Grazia Pennino

“…This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. …”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins by Senlin Tang, Fubao Sun, Wenbin Liu, Hong Wang, Yao Feng, Ziwei Li

“…Abstract Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. …”
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Advances in spatiotemporal multiscale water-energy balance models: A Review by DONG Weijie, LIU Zhiyong, CHEN Xiaohong

“…Water and energy balance models have become increasingly important for simulating hydrological and thermal processes, predicting runoff, and managing water resources over the past few decades. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. …”
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CA-STIM: an interpolation model with spatio-temporal evolution characteristics and cross-attention mechanism for 2D island morphology sequences by Peng Zhang, Wenzhou Wu, Shaochen Shi, Fengyu Li, Fenzhen Su

“…Using three coral islands in the South China Sea (Beizi, Mahuan, and Xiyue) as case studies, we perform 2D spatial morphology series interpolation. Experimental results demonstrate that our model outperforms baseline methods, achieving Dice scores of 0.9681, 0.9675, and 0.975 and Intersection-over-Union (IOU) scores of 0.9383, 0.9373, and 0.9513 on Beizi, Mahuan, and Xiyue Island, respectively.…”
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Exploring the Spatiotemporal Heterogeneity of Stream Nitrogen Concentrations in a Typical Human‐Activity‐Influenced Headwater Watershed in South China by Congsheng Fu, Haixia Zhang, Huawu Wu, Haohao Wu, Yang Cao, Ye Xia, Zichun Zhu

“…This study thoroughly explored the spatiotemporal variations in stream nitrogen concentrations in a typical headwater watershed in South China. Spatially distributed measurements were conducted during 2020–2022, and mathematical modeling was implemented based on incorporating these data. …”
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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…Therefore, it is crucial to develop automated and efficient methods for predicting therapeutic outcomes.MethodsWe have developed a predictive model for the surgical efficacy in ME patients based on deep learning and optical coherence tomography (OCT) imaging, aimed at predicting the treatment outcomes at different time points. …”
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Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis by Jin-Xin Zheng, Hui‐Hui Zhu, Shang Xia, Men‐Bao Qian, Robert Bergquist, Hung Manh Nguyen, Xiao‐Nong Zhou

“…Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. …”
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