Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The models were trained on data from 2000 to 2021, with 2022 serving as an independent case study to evaluate their prediction accuracy. …”
Get full text

A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. …”
Get full text

The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada by Qianyu Chang, Simon Zwieback, Aaron A. Berg

“…Here, we evaluate the predictive skill of XGBoost models for identifying (1) ice wedge and (2) top-5m visible ground ice in the Tuktoyaktuk Coastlands. …”
Get full text

DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…Methods We present DeepMiRBP, a novel hybrid deep learning model specifically designed to predict microRNA-binding proteins by modeling molecular interactions. …”
Get full text

Zenith Tropospheric Delay Forecasting in the European Region Using the Informer–Long Short-Term Memory Networks Hybrid Prediction Model by Zhengdao Yuan, Xu Lin, Yashi Xu, Jie Zhao, Nage Du, Xiaolong Cai, Mengkui Li

“…We then employed this interpolated data from 2016 to 2020, along with an Informer–LSTM Hybrid Prediction Model, to develop a long-term prediction model for ZTD with a prediction duration of one year. …”
Get full text

Analysis and prediction of spatiotemporal carbon storage changes in the Taihu Lake Basin in Jiangsu Province based on PLUS and InVEST model by Yu Zhu, Bing Ma, Haibo Hu, Dongxia Ding, Hongwei Zhou, Jiaxuan Liu, Jiacai Liu, Zhirong Lin

“…It aims to assess and predict spatiotemporal carbon storage changes in the Taihu Lake Basin of Jiangsu Province.thereby providing a scientific basis for regional low-carbon land-use planning under China’s ''dual carbon'' goals. …”
Get full text

Modeling Foreground Spatial Variations in 21 cm Gaussian Process Component Separation by Kangning Diao, Richard D. P. Grumitt, Yi Mao

“…Further improvements to the HGP model will require more physically-motivated modeling of foreground spatial variations. …”
Get full text

ARBOALVO: A Bayesian spatiotemporal learning and predictive model for dengue cases in the endemic Northeast city of Natal, Rio Grande do Norte, Brazil. by Mariane Branco Alves, Rafael Santos Erbisti, Aline Araújo Nobre, Taynãna César Simões, Alessandre de Medeiros Tavares, Márcia Cristina Melo, Rodrigo Moreira Pedreira, Jan Pierre Martins de Araújo, Marilia Sá Carvalho, Nildimar Alves Honório

“…These models also enable predictions to support timely interventions for arboviruses like dengue, chikungunya, and Zika.…”
Get full text

Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals by Hakan Uyanik, Mehmet Kokum, Erman Senturk, Mohamed Freeshah, Salih T. A. Ozcelik, Muhammed Halil Akpinar, Serenay Celik, Abdulkadir Sengur

“…Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. …”
Get full text

Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) by Ana C. Cuéllar, Roberto D. Coello-Peralta, Davis Calle-Atariguana, Martha Palacios-Macias, Paul L. Duque, Liliana M. Galindo, Mario O. Zaidenberg, María J. Dantur-Juri

“…Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. …”
Get full text

Developing Transferable Fourier Transform Mid-Infrared Spectroscopy Predictive Models for Buffalo Milk: A Spatio-Temporal Application Strategy Analysis Across Dairy Farms by Han Jiang, Peipei Wen, Yikai Fan, Yi Zhang, Chunfang Li, Chu Chu, Haitong Wang, Yue Zheng, Chendong Yang, Guie Jiang, Jianming Li, Junqing Ni, Shujun Zhang

“…Moreover, when using the two application strategies that predicted contemporaneous samples as the model, and adding 30–70% of the samples from the predicted farm, the model application effect can be improved before the robust model has been fully developed.…”
Get full text

Pulsed Focused Nonlinear Acoustic Fields from Clinically Relevant Therapeutic Sources in Layered Media: Experimental Data and Numerical Prediction Results by Tamara KUJAWSKA

“…The comparison of the experimental results with those simulated numerically has shown that the model based on the TAWE approach predicts well both the spatial-peak and spatial-spectral pressure variations in the pulsed focused nonlinear beams produced by the transducer used in water for all excitation levels complying with the condition corresponding to weak or moderate source-pressure levels. …”
Get full text

A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon, Kyuchul Lee

“…To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. …”
Get full text

Spatial Modeling of Douglas‐Fir Plantations in Italy After 120 Years of Experimentation by Maurizio Marchi, Martina Cocozza, Gabriele Bucci, Paolo Iovieno

Get full text

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. …”
Get full text

Breaks in the Arctic ice cover: from observations to predictions by A. A. Ershova, L. N. Dyment, T. A. Alekseeva

Get full text

Deep Learning for Predicting Biomolecular Binding Sites of Proteins by Minjie Mou, Zhichao Zhang, Ziqi Pan, Feng Zhu

“…Emerging trends in hybrid models that combine multimodal data, such as integrating sequence and structural information, along with innovations in geometric deep learning, present promising directions for improving prediction accuracy. …”
Get full text

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. …”
Get full text

Application of Machine Learning Methods for Gravity Anomaly Prediction by Katima Zhanakulova, Bakhberde Adebiyet, Elmira Orynbassarova, Ainur Yerzhankyzy, Khaini-Kamal Kassymkanova, Roza Abdykalykova, Maksat Zakariya

“…Results indicated that the Exponential GPR model demonstrated the highest predictive accuracy, outperforming other ML methods, with 72.9% of predictions having errors below 1 mGal. …”
Get full text

Unsupervised Action Anticipation Through Action Cluster Prediction by Jiuxu Chen, Nupur Thakur, Sachin Chhabra, Baoxin Li

“…These pseudo-labels are then input into a temporal sequence modeling module that learns to predict future actions in terms of pseudo-labels. …”
Get full text