How spatial resolution mediates canopy spectral diversity as a proxy for marsh plant diversity by Yi Fu, Yunlong Yao, Lei Wang, Huaihu Yi, Yuanqi Shan

“…These findings emphasize the importance of selecting appropriate spatial resolutions and SD metrics to enhance the accuracy of remote sensing-based biodiversity prediction models.…”
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Estimating the breeding population size of the short-tailed shearwater using a nesting habitat suitability model by Nicolas De Almeida E Silva, Nicolas De Almeida E Silva, Jacquomo Monk, Paco Bustamante, John P. Y. Arnould

“…While an increased spatial balance in training dataset could greatly improve the accuracy of global models, the estimate results of the present study are consistent with previous findings. …”
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Spatial interpolation and spatiotemporal scanning analysis of human brucellosis in mainland China from 2012 to 2018 by Yuan Zhao, Dongfeng Pan, Yanfang Zhang, Lixu Ma, Hong Li, Jingjing Li, Shanghong Liu, Peifeng Liang

“…ArcGIS 10.6 software was employed to perform kriging interpolation analysis and to create a predictive distribution map for brucellosis. Additionally, SaTScan software was utilized to conduct spatial-temporal scanning analysis to identify potential spatial-temporal changes in the incidence rate of brucellosis in China. …”
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Bedload transport within a patchy submerged canopy with different patch densities and spatial configuration by Hyoungchul Park, Heidi Nepf

“…Furthermore, at the same ϕp, channel-spanning patches were associated with lower bedload transport, compared to laterally unconfined patches. A predictive model for bedload transport rate that incorporated both near-bed mean velocity and TKE provided more accurate predictions than models based only on time-averaged velocity (bed stress) or TKE.…”
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Capturing Spatial-Temporal Traffic Patterns: A Dynamic Partitioning Strategy for Heterogeneous Traffic Networks by Xianyue Peng, Hao Wang

“…Macroscopic fundamental diagram (MFD) has become a popular model used in developing network traffic controls for a roughly homogeneous traffic network, encounters limitations when applied to the inherently heterogeneous nature of real-world transportation networks, affecting its predictive accuracy and applicability. …”
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Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization by Chuanwei Zhang, Dingshuai Liu, Paraskevas Tsangaratos, Ioanna Ilia, Sijin Ma, Wei Chen

“…A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. …”
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Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models by Yunbin Ma, Zuyue Shang, Jie Zheng, Yichen Zhang, Guangyuan Weng, Shu Zhao, Cheng Bi

“…Traditional leakage prediction models for long-distance pipelines have limitations in effectively synchronizing spatial and temporal features of leakage signals, leading to data processing that heavily relies on manual experience and exhibits insufficient generalization capabilities. …”
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Early Warning and Management Measures for the Non-Agriculturalization of Cultivated Land in Shaanxi Province of China Based on a Patch-Generated Land Use Simulation Model by Huiting Yan, Hao Chen, Fei Wang, Linjing Qiu, Rui Li

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A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport by M. Giselle Fernández-Godino, Wai Tong Chung, Akshay A. Gowardhan, Matthias Ihme, Qingkai Kong, Donald D. Lucas, Stephen C. Myers

“…DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. …”
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Spatial interpolation of cropland soil bulk density by increasing soil samples with filled missing values by Aiwen Li, Jinli Cheng, Dan Chen, Wendan Li, Yaruo Mao, Xinyi Chen, Bin Zhao, Wenjiao Shi, Tianxiang Yue, Qiquan Li

“…However, soil bulk density (BD) data in historical datasets is often incomplete, and it’s uncertain if filled values enhance spatial interpolation accuracy. Using 2,883 cropland soil BD samples from the Sichuan Basin in China, we developed the best prediction models from traditional pedotransfer function (PTF), multiple linear regression (MLR), random forest (RF), and radial basis function neural network (RBFNN) to fill missing BD values for 1,336 samples. …”
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Accounting for temporal and spatial autocorrelation to examine the effects of climate change on vegetation greenness trend in China by Lingwei Chen, Likai Zhu, Cuiyutong Yang, Zizhen Dong, Rui Huang, Jijun Meng, Min Liu

“…Trend and attribution analysis of vegetation greenness is crucial to explain and predict ecosystem responses to climate change. The common practice to detect and explain greenness pattern from remote sensing time series is mostly based on pixel-by-pixel analysis, which often fails to account for spatial autocorrelation and may lead to spurious patterns. …”
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Spatial variability of soil properties in red soil and its implications for site-specific fertilizer management by Fang-fang SONG, Ming-gang XU, Ying-hua DUAN, Ze-jiang CAI, Shi-lin WEN, Xian-ni CHEN, Wei-qi SHI, Gilles COLINET

“…Assessing spatial variability and mapping of soil properties constitute important prerequisites for soil and crop management in agricultural areas. …”
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Deciphering the tumor immune microenvironment: single-cell and spatial transcriptomic insights into cervical cancer fibroblasts by Zhiheng Lin, Youwei Zhou, Zhenran Liu, Wenyang Nie, Hengjie Cao, Shengnan Li, Xuanling Li, Lijun Zhu, Guangyao Lin, Yanyu Ding, Yi Jiang, Zuxi Gu, Lianwei Xu, Zhijie Zhao, Huabao Cai

“…The prognostic model incorporating fibroblast-specific markers demonstrated robust predictive power for patient survival outcomes. …”
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An upgraded high-precision gridded precipitation dataset for the Chinese mainland considering spatial autocorrelation and covariates by J. Hu, C. Miao, J. Su, Q. Zhang, J. Gou, J. Gou, Q. Sun, Q. Sun

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Using Upstream and Downstream Traffic Information for Short term Traffic Flow Prediction Based on LSTM Recurrent Neural Network by MAN Chun-tao, KANG Dan-qing

“…We employ the long/shortterm memory (LSTM) recurrent neural network to analyze the impact of various input settings on shortterm traffic flow prediction performance First, we compared the shortterm traffic flow prediction performance for different combinations of traffic flow, speed and occupancy data on the same vehicle detection station (VDS) The results show that the inclusion of occupancy/speed information may help to enhance the performance of the model as awhole In order to introduce spatial information into the model, we further include as inputs traffic variables from the upstream and/or downstream vehicle detector stations and test 16 different input combinations for traffic flow prediction The experimental results show that the inclusion of both upstream and downstream traffic information in the model is very useful for improving the accuracy of shortterm traffic flow prediction…”
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An integrated method of selecting environmental covariates for predictive soil depth mapping by Yuan-yuan LU, Feng LIU, Yu-guo ZHAO, Xiao-dong SONG, Gan-lin ZHANG

“…Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. …”
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Schistosomiasis Burden and Trend Analysis in Africa: Insights from the Global Burden of Disease Study 2021 by Dandan Peng, Yajing Zhu, Lu Liu, Jianfeng Zhang, Peng Huang, Shaowen Bai, Xinyao Wang, Kun Yang

“…Five modeling algorithms were constructed to predict disease burden in Africa from 2022 to 2041. …”
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Investigating the Development of Urban Space in the face of Environmental Hazards (Case Study of Noor County) by Hossein Sharifi, Mehrdad Ramezanipour, Leila Ebrahimi

“…According to the results of risk potential zoning, the northern and southern areas of the city have the highest risk potential. To predict the development of residential areas, the combined Markov chain model and cellular automation were used. …”
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AI-Driven Spatial Data Analysis of Groundwater Level and Gravimetric Data in Roorkee Region, India by H. Sarkar, C. S. P. Ojha, S. K. Ghosh

“…To address this gap and align with SDGs, this study aims to develop a regression-based machine learning model for spatially varying groundwater level prediction. …”
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Regional Landslide Hazard and Risk Assessment Considering Landslide Spatial Aggregation and Hydrological Slope Units by Xuetao Yi, Yanjun Shang, He Meng, Qingsen Meng, Peng Shao, Izhar Ahmed

“…Moreover, the DFR-RF model had the best prediction performance, and its predictions were adopted together with vulnerability values to calculate the landslide risk. …”
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