Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests by Sara Winsemius, Chad Babcock, Van R. Kane, Kat J. Bormann, Hugh D. Safford, Yufang Jin

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DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space by A. Giacoletti, M. Bosch-Belmar, G. Di Bona, M.C. Mangano, B. Stechele, G. Sarà

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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman

“…This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. …”
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Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

“…The model was trained using diverse geospatial parameters including topographical, hydrological, soil, and geological parameters, along with an updated landslide inventory, enabling spatially explicit predictions of landslide susceptibility. …”
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Spatial Evolution and Scenario Simulation of Carbon Metabolism in Coal-Resource-Based Cities Towards Carbon Neutrality: A Case Study of Jincheng, China by Li Zhu, Mengying Cao, Wenyuan Wang, Tianyue Zhang

“…This study focused on Jincheng City, utilizing the LOW EMISSIONS ANALYSIS PLATFORM (LEAP) model to predict carbon emissions from energy consumption under various scenarios from 2020 to 2060. …”
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Spatiotemporal Dynamics and Potential Distribution Prediction of <i>Spartina alterniflora</i> Invasion in Bohai Bay Based on Sentinel Time-Series Data and MaxEnt Modeling by Qi Wang, Guoli Cui, Haojie Liu, Xiao Huang, Xiangming Xiao, Ming Wang, Mingming Jia, Dehua Mao, Xiaoyan Li, Yihua Xiao, Huiying Li

“…This study employed multi-temporal Sentinel-1/2 imagery (2016–2022) to map and predict the spread of <i>S. alterniflora</i> in Bohai Bay. …”
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Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice by Meiling Sheng, Chunlin Li, Weixing Zhang, Jing Nie, Hao Hu, Weidong Lou, Xunfei Deng, Shengzhi Shao, Xiaonan Lyu, Zhouqiao Ren, Karyne M. Rogers, Syed Abdul Wadood, Yongzhi Zhang, Yuwei Yuan

“…In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). …”
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Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin by Junqiang Xu, Fan Wang, Chao Ren, Jianmin Bian, Tao Li, Zikai Ping

“…Study focus: In this study, we analyzed the spatial and temporal patterns of water yield and the main influencing factors of the Yiluo River Basin based on the water yield response framework constructed by the SWAT model and the intelligent optimization algorithm. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. …”
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Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China by Fanyan Ma, Xiang Huang, Zhenan Yang, Panxin Niu, Keyao Pang, Mei Wang, Guangming Chu

“…This study employed the MaxEnt model to predict the potential habitats of these three species in China under climate change, and examined the relationship between their distribution and desert ecosystems. …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. …”
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Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System by Sean W. Fleming, Karl Rittger, Catalina M. Oaida Taglialatela, Indrani Graczyk

“…We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). …”
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Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia by Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono

“…Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction and risk mapping. …”
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Spatial and temporal epidemiology of SARS-CoV-2 virus lineages in Teesside, UK, in 2020: effects of socio-economic deprivation, weather, and lockdown on lineage dynamics by Moss, E.D., Rushton, S.P., Baker, P., Bashton, M., Crown, M.R., dos Santos, R.N., Nelson, A., O’Brien, S.J., Richards, Z., Sanderson, R.A., Yew, W.C., Young, G.R., McCann, C.M., Smith, D.L.

“…The relationships between positive tests and covariates varied greatly between lineages, likely due to the strong heterogeneity in their spatial and temporal distributions. Cases during the second wave appeared to be higher in areas that recorded fewer first-wave cases, however, an additional model showed the number of first-wave cases was not predictive of second-wave cases. …”
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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

“…It was important for justifying the use of numerical modelling in designing the baffles distribution (internal walls in the flow space) for obtaining the most spatially uniform field of flow velocity.…”
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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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Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone by Yiping Dou, Nhu D. Le, James V. Zidek

“…One of these approaches adapts a multivariate method originally designed for spatial prediction. The second is based on a state-space modeling approach originally developed and used in a case study involving one week in Mexico City with ten monitoring sites. …”
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Efficient solving of Schrödinger equation using deep convolutional neural network model with an attention mechanism and transfer learning by Ziyi Zhao, Shishun Zhao, Mingjun Zhou, Yujun Yang

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Multi-omics approach reveals the impact of prognosis model-related genes on the tumor microenvironment in medulloblastoma by Dongming Han, Dongming Han, Xuan Chen, Xuan Chen, Xin Jin, Jiankang Li, Dongyang Wang, Dongyang Wang, Ziwei Wang

“…This study aimed to develop a TME-associated risk score(TMErisk) model using RNA sequencing data to predict patient outcomes and elucidate biological mechanisms.MethodsRNA sequencing data from 322 Tiantan and 763 GSE85217 MB samples were analyzed. …”
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