Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…We also replaced the linear layer in the Gated Recurrent unit with a dynamic graph convolution operation to jointly model spatial-temporal correlation. Finally, we propose a temporal fusion layer with multi-scale features to model accurate temporal semantic information from contextual environment with different window sizes, to further obtain accurate prediction results. …”
Get full text

Spatial variation of feature density in multiscale topographic data by T. E. Samsonov, O. P. Yakimova, D. A. Potemkin, O. A. Guseva

“…Resulting model predicts how much higher or lower is the expected spatial density of features over the area in comparison to the average density for the LoD. …”
Get full text

Study and Prediction of Land Use/Cover Change in Small and Medium-Sized River Basins in Typical Urbanization Areas by LI Liujie, LIU Bingjun, WEI Qiuying

Get full text

Spatial Correlates of Dementia and Disability After Intracerebral Hemorrhage by Yutong Chen, Cyprien A. Rivier, Samantha A. Mora, Victor Torres Lopez, Sam Payabvash, Kevin Sheth, Andreas Harloff, Guido J. Falcone, Jonathan Rosand, Ernst Mayerhofer, Christopher D. Anderson

Get full text

Spatial distribution shifts of Batocera horsfieldi (Coleoptera: Cerambycidae) and its natural enemies in China under climate change: Implications for biological control by Liang Zhang, Chaokun Yang, Ping Wang, Guanglin Xie, Wenkai Wang

“…The aim of this study was to assess the potential suitable areas for Batocera horsfieldi (Coleoptera: Cerambycidae) and its natural enemies, Dendrocopos major (Piciformes: Picidae) and Scleroderma guani (Hymenoptera: Bethylidae), in the context of climate change and to explore the feasibility of pre-emptive classical biological control strategies. Predictions by ecological niche modelling combined with current and future bioclimatic factors showed that under future climate scenarios, the suitable areas for B. horsfieldi showed a gradual contraction, while the suitable areas for D. major and S. guani were expanding. …”
Get full text

AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology by Emran Aljarrah

Get full text

Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation by Bouthaina Slika, Fadi Dornaika, Karim Hammoudi

“…Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. <b>Methods:</b> In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. …”
Get full text

Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes by Kamilla Kurucz, Cayelan C. Carey, Peisheng Huang, Eduardo R. De Sousa, Jeremy T. White, Matthew R. Hipsey

Get full text

Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model by Paul Royer-Gaspard, François Bourgin, Charles Perrin, Vazken Andréassian, Alban De Lavenne, Guillaume Thirel, François Tilmant

Get full text

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

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary by Tamás Deák, András Dobai, Zoltán Károly Kovács, Ferenc Molnár, Endre Dobos

“…The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. …”
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

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

“…Finally, the Informer model — equipped with attention mechanisms — identifies long-term temporal dependencies, selecting the most informative timesteps for accurate prediction.The study’s experiments were conducted using a comprehensive dataset sourced from the Photovoltaic Geographical Information System (PVGIS). …”
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

Climate-Driven Invasion Risks of Japanese Beetle (<i>Popillia japonica</i> Newman) in Europe Predicted Through Species Distribution Modelling by Giuseppe Pulighe, Flavio Lupia, Valentina Manente

Get full text

Modelling height to crown base using non-parametric methods for mixed forests in China by Zeyu Zhou, Huiru Zhang, Ram P. Sharma, Xiaohong Zhang, Linyan Feng, Manyi Du, Lianjin Zhang, Huanying Feng, Xuefan Hu, Yang Yu

“…BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted.…”
Get full text

Development of a prognostic immune cell-based model for ovarian cancer using multiplex immunofluorescence by Sai Li, Boyang Jiang, Hongying Zhou, Sifu Yang, Liu Yang, Yupeng Hong

“…Conclusion This study highlights the prognostic value of immune cell spatial distribution in ovarian cancer. The ICRPI model, integrating immune cell markers and spatial analysis, provides a novel framework for predicting patient outcomes. …”
Get full text

Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region by Anik Naha Biswas, Yee Hui Lee, Wei Tao Yeo, Wai Chong Low, Ding Yu Heh, Shilpa Manandhar

Get full text