Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data by S. Daenens, S. Daenens, S. Daenens, T. Verstraeten, T. Verstraeten, T. Verstraeten, P.-J. Daems, P.-J. Daems, A. Nowé, J. Helsen, J. Helsen, J. Helsen

“…<p>This paper introduces a novel model for predicting wind turbine power output in a wind farm at a high temporal resolution of 30 s. …”
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Impact of wildfires on spatial and temporal evolution of groundwater recharge in an Atlantic pine forest: An integrated approach using field, remote sensing and modeling. by Mariana La Pasta Cordeiro, João Pedro Nunes, Maria Teresa Condesso de Melo

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Deep Learning and Time Series for the Prediction of Monthly Precipitation. A Case Study in the Department of Boyacá, Colombia by Yesid Esteban Duarte, Marco Javier Suárez Barón, Oscar Javier García Cabrejo, César Augusto Jaramillo Acevedo, Carlos Augusto Meneses Escobar

“…Context: This paper proposes a method for the prediction of monthly precipitation in the department of Boyacá using models based on deep neural networks (DNNs). …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

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Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate by Milad Jajarmizadeh, Sobri bin Harun, Shamsuddin Shahid, Shatirah Akib, Mohsen Salarpour

“…SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. …”
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AMSformer: A Transformer for Grain Storage Temperature Prediction Using Adaptive Multi-Scale Feature Fusion by Qinghui Zhang, Weixiang Zhang, Quanzhen Huang, Chenxia Wan, Zhihui Li

“…However, current prediction methods lead to information redundancy when capturing temporal and spatial dependencies, which diminishes prediction accuracy. …”
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Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. by M Isabel Ramos, Juan J Cubillas, Ruth M Córdoba, Lidia M Ortega

“…The relevance of this work is the early availability of predicted crop yield data together with the multi-scale applicability of the predictive models. …”
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A Unified Framework for Fault and Performance Prediction Using Spatio-Temporal Geometric Features Based on STSFE by Dong-Hyun Kang, A-Youn Yang, Jong-Min Lee, Jong-Gu Lee

“…The core methodology, Spatio-Temporal Slope Feature Extraction (STSFE), transforms irregular time-series data into slope-, area-, and volume-based representations, capturing both temporal dynamics and spatial correlations. We develop three distinct yet structurally aligned prediction models: 1) passive MUX fault classification, 2) SFP port-level fault detection, and 3) regression-based forecasting of Rx signal degradation. …”
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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

“…Neural network models based on GNNs often achieve good results in traffic flow prediction tasks of traffic networks. …”
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Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance Computing for Enhanced Flood Risk Prediction and Assessment by U. Dutta, Y. K. Singh, T. S. M. Prabhu, G. Yendargaye, R. Kale, M. K. Khare, B. Kumar, R. Panchang

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GEOGRAPHICALLY WEIGHTED MACHINE LEARNING MODEL FOR ADDRESSING SPATIAL HETEROGENEITY OF PUBLIC HEALTH DEVELOPMENT INDEX IN JAVA ISLAND by Muhammad Azis Suprayogi, Bagus Sartono, Khairil Anwar Notodiputro

“…Our results show that the non-parametric GW-RF model shows high potential for explaining spatial heterogeneity and predicting PHDI versus a global model when including six major risk factors. …”
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Assimilation of Doppler Radar Data and Its Impact on Prediction of a Heavy Meiyu Frontal Rainfall Event by Hongli Li, Xiangde Xu, Yang Hu, Yanjiao Xiao, Zhibin Wang

“…Operational Doppler radar observations have potential advantages over other above-surface observations when it comes to assimilation for mesoscale model simulations with high spatial and temporal resolution. …”
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A Regional Investigation of Inverse Distance Weighting Particulate Matter Prediction within Kirkuk City, Iraq by Huda Jamal Jumaah, Wafaa Abbas Hasan, Sarah Jamal Jumaah

“…The results indicated a good fit for the prediction determined by the analysis. Moreover, the health risks have also been detected from the spatial distribution of each pollutant. …”
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A Prediction-Based Anomaly Detection Method for Traffic Flow Data with Multi-Domain Feature Extraction by Xianguang Jia, Jie Qu, Yingying Lyu, Mengyi Guo, Jinke Zhang, Fengxiang Guo

“…The prediction model is built as follows: first, Bidirectional Long Short-Term Memory network (Bi-LSTM) and a Graph Attention Network (GAT) extract temporal and spatial features, respectively. …”
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Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field by Xianbing Li, Rui Liu, Dong Ma, Yang Bai, Yukai Shi, Badm Anhouck

“…Comparative analysis with other swarm intelligence algorithms shows that the improved particle swarm algorithm has stronger convergence ability and higher prediction accuracy than the BP model and SVM model. …”
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Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field by Changyu Su, Bishnupriya Sahoo, Miaohua Mao, Meng Xia

“…Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. …”
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What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square by Jumana Hamdani, Pablo Antuña Molina, Lucía Leva Fuentes, Hesham Shawqy, Gabriella Rossi, David Andrés León

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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…These methods collectively optimize prediction accuracy and resource management. The result shows that the suggested model significant improvement in precision, recall, and F1-Score for crop yield, reaching 93 %, 94 %, and 93 %, implemented using Python software. …”
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Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model by Bijoy Saha, Mahmudur Rahman Fatmi, Nazmul Arefin Khan

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Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer by Taishi Nishizawa, Takouhie Maldjian, Zhicheng Jiao, Tim Q. Duong

“…The model integrates 3D convolutional neural networks and self-attention to capture spatial and cross-modal interactions. …”
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