IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…The primary objective was to develop a predictive model that improves traffic forecasting accuracy, reduces congestion, and optimizes real-time traffic management. …”
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A cluster-based local modeling paradigm for high spatiotemporal resolution VPD prediction using multi-source data and machine learning by Mi Wang, Zhuowei Hu, Xiangping Liu, Wenxing Hou

“…The results show that the local modeling significantly enhances prediction accuracy, with the XGBoost model outperforming others across all clusters and maintaining high precision across seasons scales and different land use types. …”
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Modeling robustness tradeoffs in yeast cell polarization induced by spatial gradients. by Ching-Shan Chou, Qing Nie, Tau-Mu Yi

“…In this work, we investigated the tradeoffs among these performance objectives using a generic model that captures the basic spatial dynamics of polarization in yeast cells, which are small. …”
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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. …”
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ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model by Y. Liu, H. Huang, S.-C. Wang, T. Zhang, D. Xu, Y. Chen

“…A Fortran–C–Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. …”
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Unifying spatiotemporal and frequential attention for traffic prediction by Qi Guo, Qi Tan, Jun Tang, Benyun Shi

“…By leveraging deep learning to capture spatial correlations in traffic flow and applying spectral analysis to fuse time series data with underlying periodic correlations in both the time and frequency domains, we develop an innovative traffic prediction model called the Space-Time-Frequency Attention Network (STFAN). …”
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Intelligent Prediction and Numerical Simulation of Landslide Prediction in Open-Pit Mines Based on Multi-Source Data Fusion and Machine Learning by Li Qing, Linfeng Xu, Juehao Huang, Xiaodong Fu, Jian Chen

“…Five machine learning models for landslide prediction are compared using this dataset. …”
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Regularity and predictability of human mobility in personal space. by Daniel Austin, Robin M Cross, Tamara Hayes, Jeffrey Kaye

“…Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. …”
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Predicting changes in maximum temperatures in the mid-future period in Sistan and Baluchestan under SSP scenarios by abdolreza kashki, ghorban jafari

“…IDW interpolation in GIS was used to map spatial temperature changes. Paired-sample t-tests evaluated differences between baseline and mid-future periods.Finding: The CanESM5 model performed best in predicting temperature changes. …”
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Modeling the Effects of Spatial Heterogeneity and Seasonality on Guinea Worm Disease Transmission by Anthony A. E. Losio, Steady Mushayabasa

“…The model incorporates seasonal variations, educational campaigns, and spatial heterogeneity. …”
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Extending isolation by resistance to predict genetic connectivity by Robert J. Fletcher Jr, Jorge A. Sefair, Nicholas Kortessis, Roldolfo Jaffe, Robert D. Holt, Ellen P. Robertson, Sarah I. Duncan, Andrew J. Marx, James D. Austin

“…This framework extends isolation‐by‐resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes.…”
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Constraint-incorporated deep learning model for predicting heat transfer in porous media under diverse external heat fluxes by Ziling Guo, Hui Wang, Huangyi Zhu, Zhiguo Qu

“…The temperature field within porous media is considerably affected by different boundary conditions, and effective thermal conductivity varies with spatial structure morphologies. At present, traditional prediction methods for the temperature field are expensive and time consuming, particularly for large structures and dimensions, whereas deep learning surrogate models have limitations related to constant boundary conditions and two-dimensional input slices, lacking the three-dimensional topology and spatial correlations. …”
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Digital agriculture drives carbon emission reduction in China by Yao Wenyan, Li Chenghan, Lin Fangzhou

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Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets by Mengxin Bai, Peng Zhang, Pei Xing, Wupeng Du, Zhixin Hao, Hui Zhang, Yifan Shi, Lulu Liu

“…Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. …”
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Post-Disaster Recovery Effectiveness: Assessment and Prediction of Coordinated Development in the Wenchuan Earthquake-Stricken Areas by Liang Zhao, Chunmiao Zhang, Xia Zhou

“…By constructing a framework to assess post-disaster coordinated development, this study utilized the entropy weight method and mean-variance method for the comprehensive weighting of evaluation indicators. The gray system prediction model G(1,1) was used to forecast the coordinated development levels of the three cities from 2019 to 2025. …”
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Prediction of Energetic Electrons in the Inner Radiation Belt and Slot Region With a Double‐Layer LSTM Neural Network Model by Ling Yang, Liuyuan Li, Jinbin Cao

“…Here, we trained a double‐layer long short‐term memory (LSTM) neural network model and successfully predicted the spatial and temporal variations of the 108–749 keV electrons in the inner radiation belt (L ∼ 1.2–2.2) and slot region (L ∼ 2.2–3.2). …”
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A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models by Unurnyam Jugnee, Le Jiao, Sainbayar Dalantai, Lili Huo, Yi An, Bayartungalag Batsaikhan, Undrakhtsetseg Tsogtbaatar, Munguntuul Ulziibaatar, Boldbaatar Natsagdorj

“…Current researches about heavy metal pollution mainly focus on source apportionment, while robust and accurate predictions on its spatial distribution and driving mechanisms is still lacking. …”
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An equivalent and simplified approach for acoustic noise prediction in a PM synchronous motor based on the semi‐analytical‐FEM model by Armin Saki, Arash Kiyoumarsi, Alireza Ariaei

“…Based on this approach, the simplest and most adequate semi‐analytical‐FEM model for noise prediction in PMSMs is proposed. …”
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Global distribution prediction and ecological conservation of basking shark (Cetorhinus maximus) under integrated impacts by Runlong Sun, Kaiyu Liu, Wenhao Huang, Xiao Wang, Hongfei Zhuang, Zongling Wang, Zhaohui Zhang, Linlin Zhao

“…This study employs various environmental variables and distribution data to construct a global species distribution model for basking sharks, predicting their distribution patterns under current and future climate scenarios. …”
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SD-LSTM: A Dynamic Time Series Model for Predicting the Coupling Coordination of Smart Agro-Rural Development in China by Chunlin Xiong, Yilin Zhang, Weijie Wang

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