Advanced air quality prediction using multimodal data and dynamic modeling techniques by Umesh Kumar Lilhore, Sarita Simaiya, Rajesh Kumar Singh, Abdullah M. Baqasah, Roobaea Alroobaea, Majed Alsafyani, Afnan Alhazmi, M. D. Monish Khan

“…The attention mechanism directs the model’s focus to the most informative features, improving predictive accuracy. …”
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Buzzing towards Resilience: Investigating the Spatial Alignment of the Desert Pallid Bee, <i>Centris pallida</i>, and Its Host Plants in Response to Climate Change by Terese Maxine P. Cruz, Stephen L. Buchmann, Kathleen L. Prudic

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Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD by Zhiyao Gao, Katherine Duberg, Stacie L. Warren, Li Zheng, Stephen P. Hinshaw, Vinod Menon, Weidong Cai

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A mixed modeling approach to predict the effect of environmental modification on species distributions. by Francesco Cozzoli, Menno Eelkema, Tjeerd J Bouma, Tom Ysebaert, Vincent Escaravage, Peter M J Herman

“…Sustainable development requires the ability to predict responses of species to anthropogenic pressures. …”
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Leveraging Spatial and Temporal Data to Predict Heavy Freight Vehicle Traffic Flow on Rural Road Network by Alireza Gholami, Seyedehsan Seyedabrishami

“…The extreme gradient boosting (XGBoost) model surpasses the time-series model in predictive accuracy, yielding average R-squared values of 84.7% and 85.8% on the test data for trucks and tractor-trailers, respectively. …”
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Digitally mapping soil health at regional scale: disentangling drivers and predicting spatial land use effects by Valentina Rubio, Joseph Amsili, David G. Rossiter, Andrew McDonald, Harold van Es

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Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging by Laimou Lu, Penghui Li, Liang Zhong, Mingbao Luo, Liyuan Xing, Chunlai Zhang

“…This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. …”
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SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction by Xiaoyong Tan, Kaiqi Chen, Min Deng, Baoju Liu, Zhiyuan Zhao, Youjun Tu, Sheng Wu

“…However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. …”
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Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States by Hongxiang Yan, Ning Sun, Hisham Eldardiry, Travis Thurber, Patrick Reed, Daniel Kennedy, Sean Swenson, Jennie Rice

“…Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. …”
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Advanced Heart Disease Prediction Through Spatial and Temporal Feature Learning with SCN-Deep BiLSTM by Vivek Pandey, Umesh Kumar Lilhore, Ranjan Walia

“…Abstract Heart disease prediction using machine learning methods faces various challenges, such as low data quality, missing irrelevant values, and underfit and overfit problems, which increase the time complexity and degrade the model's prediction performance. …”
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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution by Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG

“…Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.…”
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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. …”
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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. …”
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Method and experimental verification of spatial attitude prediction for an advanced hydraulic support system under mining influence by Zhuang Yin, Kun Zhang, ZengBao Zhang, Hongyue Chen, Lingyu Meng, Zhen Wang, Mingchao Du, Xiangpeng Hu, Defu Zhao, Dan Tian

“…A spatial attitude prediction method for the advanced hydraulic support group based on WOA-LSTM was proposed. …”
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Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis. by Chao Hu, Fan Wang, Hui Xu, XiQi Dong, XiuJuan Xiong, Yun Zhang, TianCheng Zhao, YuanQiao He, LiBin Deng, XiongBing Lu

“…Lymph_inside % can be an effective biomarker for predicting MIBC prognosis. This study suggests a novel approach for the development of new prognostic biomarkers based on the spatial distribution of lymphocyte aggregates.…”
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Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

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Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis by Huibo Zhang, Ming Luo, Junwei Feng, Juan Tan, Yan Jiang, Dmitrij Frishman, Yang Liu

“…Random forest models incorporating clinical/pathological data with (M1) and without (M2) TIL features (TIL scores and sTILCs) were developed on a training cohort (N = 312) to predict LNM, and performance was compared across validation (N = 78) and independent test cohorts (N = 148). …”
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A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction by Jian Wang, Yi‐ran Liu, Ya‐fei Shi

“…Abstract In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. …”
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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms by Hu Sun, Ward Manchester IV, Yang Chen

“…We found that using the new features can improve the skill scores of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. …”
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