Cognitive and Spatial Forecasting Model for Maritime Migratory Incidents: SIFM by Donatien Agbissoh Otote, Antonio Vázquez Hoehne

Get full text

Predicting suitable habitats for Asian elephant (Elephas maximus) in Tropical Asia under changing climatic scenarios by Kazi Al Muqtadir Abir, Biplob Dey, Mohammad Redowan, Ashraful Haque, Romel Ahmed

Get full text

Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. …”
Get full text

Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

Get full text

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

Get full text

Multidimensional assessment of the spatiotemporal evolution, driving mechanisms, and future predictions of urban heat islands in Jinan, China by Lingye Tan, Tiong Lee Kong Robert, Yan Zhang, Siyi Huang, Ziyang Zhang

“…By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. …”
Get full text

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). …”
Get full text

Transformer based models with hierarchical graph representations for enhanced climate forecasting by T. Bhargava Ramu, Raviteja Kocherla, G. N. V. G. Sirisha, V. Lakshmi Chetana, P. Vidya Sagar, R. Balamurali, Nanditha Boddu

“…The model integrates three key components: Spatial-Temporal Fusion Module (STFM) to capture spatiotemporal dependencies, Hierarchical Graph Representation and Analysis (HGRA) to model structured climate relationships, and Dynamic Temporal Graph Attention Mechanism (DT-GAM) to enhance temporal feature extraction. …”
Get full text

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. …”
Get full text

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. …”
Get full text

<i>Solanum macrocarpon</i> L. Ethanolic Leaf Extract Exhibits Neuroprotective and Anxiolytic Effects in Scopolamine-Induced Amnesic Zebrafish Model by Ion Brinza, Corina Guliev, Ibukun Oluwabukola Oresanya, Hasya Nazli Gok, Ilkay Erdogan Orhan, Lucian Hritcu

Get full text

Predicting Forest Evapotranspiration using Remote Sensing and Machine Learning by B. Yadav, L. K. Sharma, B. Bijarniya

Get full text

Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
Get full text

A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…However, traditional models struggle to capture the complex spatial and temporal dependencies in irradiance data, limiting prediction accuracy under varying weather conditions. …”
Get full text

Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales by Yujin Zhao, Bernhard Schmid, Zhaoju Zheng, Yang Wang, Jin Wu, Yao Wang, Ziyan Chen, Xia Zhao, Dan Zhao, Yuan Zeng, Yongfei Bai

“…Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. …”
Get full text

GIS-based calculation method to predict mining subsidence in flat and inclined mining: A comparative case study by Ibrahim Djamaluddin, Poppy Indrayani, Yue Cai, Yujing Jiang

“…Many calculation models are used to predict mining subsidence. A comprehensive method to render current calculation models superfluous can only come from a theoretical model, but the challenge remains in defining the parameters, given the great variety of rock structures found. …”
Get full text

Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application by Wei‐Ting Hung, Barry Baker, Patrick C. Campbell, Youhua Tang, Ravan Ahmadov, Johana Romero‐Alvarez, Haiqin Li, Jordan Schnell

“…FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). …”
Get full text

Evaluation and Prediction of Wind Power Utilization Efficiency Based on Super-SBM and LSTM Models: A Case Study of 30 Provinces in China by Chengyu Li, Qunwei Wang, Peng Zhou

“…This study establishes the improved super-efficiency slack-based measure (Super-SBM) model and long short-term memory (LSTM) network models, systematically and comprehensively measures and predicts the wind power utilization efficiency of 30 regions in China from 2013 to 2020, and explores regional differences in wind power utilization efficiency. …”
Get full text

A data-driven reduced-order model for fast prediction of resonant acoustic flow under vertical vibration based on secondary decomposition by Yuqi Gao, Ning Ma, Shifu Zhu, Pengchao Zhang, Hongxing Liu, Zhongyuan Xie

“…The original dataset is derived from an experimentally validated computational fluid dynamics model. The flow field snapshots are decomposed into spatial modes and temporal coefficients using proper orthogonal decomposition. …”
Get full text

Modeling spatial distributions of Amah Mutsun priority cultural plants to support Indigenous cultural revitalization by Annalise Taylor, Alexii Sigona, Maggi Kelly

“…We utilized community science datasets with an ensemble modeling approach that combines the results of five machine learning models to predict not only the distribution of each species, but also the relative certainty of those predictions spatially. …”
Get full text