Development of Laser Underwater Transmission Model from Maximum Water Depth Perspective by Guoqing Zhou, Kun Li, Jian Gao, Junyun Ma, Ertao Gao, Yanling Lu, Jiasheng Xu, Xiao Zhou

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Generalizable Storm Surge Risk Modeling by Mahlon Scott, Hsin-Hsiung Huang

“…Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. …”
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The impact of spatiotemporal variability of environmental conditions on wheat yield forecasting using remote sensing data and machine learning by Keltoum Khechba, Mariana Belgiu, Ahmed Laamrani, Alfred Stein, Abdelhakim Amazirh, Abdelghani Chehbouni

“…This study aims to assess the impact of spatial and temporal heterogeneity of environmental conditions on wheat yield forecasting using machine learning models. …”
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A novel telomere-associated genes signature for the prediction of prognosis and treatment responsiveness of hepatocellular carcinoma by Kuo Kang, Hui Nie, Weilu Kuang, Xuanxuan Li, Yangying Zhou

“…Conclusion In this study, we developed a novel prognostic model comprising 18 TRGs for HCC, which exhibited remarkable accuracy in predicting HCC patients’ prognosis. …”
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Spatial model of international tourism and tourists' original motives by Oleksandr Korol, Tetiana Skutar

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Prediction of spatiotemporal evolution and zoning of ecological sensitivity in the upper reaches of Minjiang river, sichuan, China by Lingfan Ju, Yan Liu, Shunduo Liu, Qing Xiang, Wenkai Hu, Peng Yu

“…In this study, an ecological sensitivity index system is established to quantitatively analyze the interrelationships of ecological factors. The CA-MC model and center of gravity migration are used to investigate the spatial and temporal evolution of ecological sensitivity in the upper Minjiang River basin from 2000 to 2020 and to predict the ecological sensitivity in 2040. …”
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Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder by Haoyang Chen, Na Li, Hangguan Shan, Eryun Liu, Zhiyu Xiang

“…Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. …”
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Short-term displacement prediction for newly established monitoring slopes based on transfer learning by Yuan Tian, Yang-landuo Deng, Ming-zhi Zhang, Xiao Pang, Rui-ping Ma, Jian-xue Zhang

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Spatial Association Network of Land-Use Carbon Emissions in Hubei Province: Network Characteristics, Carbon Balance Zoning, and Influencing Factors by Yong Huang, Zhong Wang, Heng Zhao, Di You, Wei Wang, Yanran Peng

“…This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. …”
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Digital Mapping of Soil Equivalent Calcium Carbonate Using Landsat 8 Satellite Images and Environmental Data by Machine Learning Models in Badr Watershed, Kurdistan Province by M. Zarinibahador

“…The present study aimed to digitally map calcium carbonate equivalent using auxiliary environmental variables, Landsat 8 satellite images, and predictive models and to present the best models in the Badr watershed in the south of Qorveh district. …”
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Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China by Fanyan Ma, Xiang Huang, Zhenan Yang, Panxin Niu, Keyao Pang, Mei Wang, Guangming Chu

“…This study employed the MaxEnt model to predict the potential habitats of these three species in China under climate change, and examined the relationship between their distribution and desert ecosystems. …”
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Prediction of the Morphological Characteristics of Asymmetric Thaw Plate of Qinghai–Tibet Highway Using Remote Sensing and Large-Scale Geological Survey Data by Jianbin Hao, Zhenyang Zhao, Jianbing Chen, Zhiyun Liu, Fuqing Cui, Xiaona Liu, Wenting Lu, Jine Liu

“…Through integrating remote sensing data and large-scale geological survey results with an earth–atmosphere coupled numerical model and a random forest (RF) prediction framework, we assessed the spatial distribution of thaw asymmetry along the permafrost section of the QTH. …”
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Real-time prediction of port water levels based on EMD-PSO-RBFNN by Lijun Wang, Shenghao Liao, Sisi Wang, Jianchuan Yin, Ronghui Li, Jingyu Guan

“…Subsequently, PSO was applied to fine-tune the center and spread parameters of the RBFNN, thereby enhancing the model’s predictive performance. The optimized PSO-RBFNN model was employed to make predictions on the decomposed sub-series. …”
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Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling. by Shuoying Ning, Jiufeng Wei, Jinian Feng

“…Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potentially suitable climactic areas but also to predict where the pest is likely to spread in the future so that appropriate monitoring and management programs can be developed. …”
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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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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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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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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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