Shoreline Change Sensitivity in The Kocasu Stream Delta and The Future (2033 and 2043) Shore Change Predictions by Sultan Murat Uzun

“…According to the temporal prediction modelling, it is predicted that erosion will continue at the mouth of the Kocasu River.…”
Get full text

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal scales, which restricts effective future flow prediction; (2) reliance on predefined graph structures in graph neural networks, making it challenging to accurately model the spatial relationships in complex road networks; and (3) end-to-end training, which often results in unclear optimization directions for model parameters, thereby limiting improvements in predictive performance. …”
Get full text

Runoff Prediction and Uncertainty Analysis for Xijiang River Basin Based on CMIP6 Climate Scenarios by WU Huiming, YAN Meng, ZHOU Shuai

“…Furthermore, by utilizing data from 15 climate models under CMIP6, the bias correction and spatial disaggregation (BCSD) downscaling method is applied to downscale the data to the Xijiang River Basin. …”
Get full text

Prediction of Dissolved Gas Concentration in Transformer Oil Based on Hybrid Mode Decomposition and LSTM-CNN by Tie CHEN, Zhifan ZHANG, Xianshan LI, Yifu CHEN, Hongxin LI

“…Finally, in order to enhance the fitting of the model to the temporal and spatial features of the sequence, the TA-LSTM-CNN is used to predict the decomposition components and reconstruct the gas concentration data. …”
Get full text

Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review by Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj, Dino Dobrinić

“…This review provides a brief overview of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent advancements in machine learning techniques have further enhanced LSP prediction capabilities, offering promising approaches for short-term prediction of vegetation phenology and cropland suitability assessment. …”
Get full text

GLC Prediction of Dioxin-Furan and Metals Emissions from a Hazardous Waste Incineration Plant by Afsaneh Afzali, M. Rashid

“…Spatial prediction and evaluation of pollutants emissions from incineration plant can be assessed by using air dispersion models. …”
Get full text

MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction by Lingfeng Wang, Yinghong Li, Yaozheng Zhou, Liping Guo, Congzhou Chen

“…In this study, we propose the Multi-view Feature Embedding for drug-drug interaction prediction (MFE-DDI), which integrates SMILES information, molecular graph data and atom spatial semantic information to model drugs from multiple perspectives and encapsulate the intricate drug information crucial for predicting DDIs. …”
Get full text

Hybrid Neural Network Approach with Physical Constraints for Predicting the Potential Occupancy Set of Surrounding Vehicles by Bin Sun, Shichun Yang, Jiayi Lu, Yu Wang, Xinjie Feng, Yaoguang Cao

“…Furthermore, a mixture density network (MDN) is employed to estimate predictive uncertainty, transforming deterministic trajectory predictions into spatial probability distributions. …”
Get full text

Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series by V. Egea-Cobrero, V. Rodriguez-Galiano, E. Sánchez-Rodríguez, M.A. García-Pérez

Get full text

Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

“…This study aims to enhance the performance and in-game success in women’s football by developing machine learning (ML) models that predict match outcomes based on player and team behaviour following goals. …”
Get full text

Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources by Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia, Vipin Kumar

“…Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. …”
Get full text

Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis by S. Edwin Raja, J. Sutha, P. Elamparithi, K. Jaya Deepthi, S.D. Lalitha

“…The task of predicting liver tumors is critical as part of medical image analysis and genomics area since diagnosis and prognosis are important in making correct medical decisions. …”
Get full text

Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…Environmental variables like temperatures, humidity and soil conditions; as well as features of the leaves, including their texture and shape were given as input features to the trained models. To increase the spatial resolution and coverage of our predictions, we also included remote sensing data and imagery from various drones, satellites, which generate data from all over the nation.As it turns out, these machine learning models do a far better job predicting the onset of diseases in palm oil trees than typical statistical methods. …”
Get full text

A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data by Yinghao Tan, Junfeng Wu, Yihang Liu, Shiyu Shen, Xia Xu, Bin Pan

“…Apart from slow conventional numerical weather prediction methods, recently developed deep learning methods often fail to fully integrate spatial information of the high-dimensional data and require a significant amount of computational resources. …”
Get full text

Predicting current and future potential distribution of Changnienia amoena in China under global climate change by Xingjian Liu, Qimeng Sun, Tingting Li, Shu’an Wang, Jiahao Shen, Yueqi Sun, Mimi Li

“…The dominant environmental variables influencing its distribution were also identified. The MaxEnt model yielded an AUC of 0.990 and CBI of 0.959, indicating extremely high predictive accuracy. …”
Get full text

Dynamic behavior analysis of copper wire for predicting defects in the winding process of a motor coil by Masato NAGANO, Taichi HIROSAWA, Hidefumi WAKAMATSU, Yoshiharu IWATA, Hironori SUZUKI, Takumi NAKAUE, Takahiro TANAKA

“…Therefore, this paper proposes a simulation method for the dynamic deformation of copper wire in the winding process to predict defects in the motor coil manufacturing process. …”
Get full text

Using Structural Class Pairing to Address the Spatial Mismatch Between GEDI Measurements and NFI Plots by Nikola Besic, Sylvie Durrieu, Anouk Schleich, Cedric Vega

“…Beginning with the prediction of profile structural classes and shapes on NFI plots, the proposed method ultimately projects actual measurements onto the NFI plot sites through profile pairing within the predicted structural classes. …”
Get full text

Fusion of DS-InSAR and THPF-LSTM for monitoring and predicting surface deformation in closed mines by Jianyang ZHANG, Hongdong FAN, Xiangyang ZHU, Minghu SUN

“…Due to the lack of supervision of mine closure, the spatial and temporal evolution of surface deformation and prediction and warning models are not well studied. …”
Get full text

An immune-related signature based on molecular subtypes for predicting the prognosis and immunotherapy efficacy of hepatocellular carcinoma by Xuhui Sun, Wenlong Jia, Huifang Liang, Henghui Cheng

“…Clinical pathological signature, tumor microenvironment and mutation analysis also proved the effective prediction of this model. Spatial transcriptome analysis shows that STC2 and BIRC5 are mainly enriched in liver cancer cells and their mRNA and protein expression levels were greater in higher malignant HCC cell lines than in the lower ones. …”
Get full text

HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction by Yiying Wei, Xiangyu Zeng, Xirui Chen, Hui Zhang, Zhengan Yang, Zhicheng Li

“…In comparison to benchmark models, HTSA-LSTM achieved a 20.72% reduction in the root mean square error (RMSE) and a 24.98% reduction in the negative log likelihood (NLL) for 5 s predictions of long-term trajectories. …”
Get full text