Evaluation of Ecological Carrying Capacity in Qinghai Province Based on DPSIR-TOPSIS Model by Li Xiaoyong, Jia Haifa, Zhao Wanmei

“…[Objective] The ecological carrying capacity of Qinghai Province and its cities and prefectures were quantitatively measured to reveal its spatial and temporal evolution characteristics, in order to provide a reference for the sustainable development of Qinghai Province. …”
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Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River by Jianzhong Guo, Daozhu Xu, Jian Xu, Ruoxin Zhu, Ning Li

“…This study proposed a new Spatial-Temporal Comprehensive Evaluation of Eco-environment Quality Index (SCEQI) model. …”
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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation by C. R. Corona, T. S. Hogue, T. S. Hogue

“…The aim of this work is threefold: first, to provide a concise review of the use of ML algorithms in SWT modeling and prediction; second, to review ML performance evaluation metrics as they pertain to SWT modeling and prediction to find the commonly used metrics and suggest guidelines for easier comparison of ML performance across SWT studies; and, third, to examine how ML use in SWT modeling has enhanced our understanding of spatial and temporal patterns of SWT and examine where progress is still needed.…”
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Space-Shaping Through Rhythmic Interventions for Teaching and Learning: Pedagogical Perspectives by Robin A. Chang, Pinar Sefkatli

“…Our cases wrangle with the relationships between socio-spatial and temporal scales that steer or constrain rhythmic patterns expressed in students’ analyses and design interventions. …”
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Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau by Yinghan Sang, Hong-Li Ren, Mei Li

“…MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. …”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…In response to these limitations, we propose a novel deep learning framework that effectively captures both temporal and spatial characteristics of group interactions. …”
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A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks by Ye Hong, Yanan Xin, Simon Dirmeier, Fernando Perez-Cruz, Martin Raubal

“…The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. …”
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AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review by Tiong Kah Yong, Zhenliang Ma, Carl-William Palmqvist

“…To bridge this gap, the study conducted a systematic literature review on data-driven train delay prediction models and introduced the novel AP-GRIP (Accuracy, Precision, Generalisability, Robustness, Interpretability, Practicality) evaluation framework. The framework covers six key aspects across overall, spatial, temporal, and train-specific dimensions, providing a systematic approach for the comprehensive assessment of train delay prediction models. …”
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Site selection evaluation of transmission lines in desert areas based on wind-sand physics by Moxian Dai, Moxian Dai, Yanbing Wang, Shu Zhu, Hongchao Dun, Hongchao Dun

“…The study findings reveal that global desert sand movement exhibits distinct spatial distribution patterns. Temporally, all monitored regions demonstrate a continuous intensification of sand drift potential, with South America showing the most rapid growth rate, while Asia and Africa maintain relatively stable patterns. …”
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Coordinated Development and Spatiotemporal Evolution Trends of China’s Agricultural Trade and Production from the Perspective of Food Security by Yueyuan Yang, Chunjie Qi, Yumeng Gu, Cheng Gui

“…On this basis, a coupling coordination degree model and Moran’s I indices are used to analyze the coordinated development level’s temporal changes and spatial effects. The research finds that the development levels of China’s agricultural trade and production show an upward trend but currently still exhibit the pattern of higher levels in Eastern China and lower levels in Western China. …”
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Evaluation and driving force analysis of relative effectiveness in the giant panda national park in Sichuan, China by Bao Meng, Bao Meng, Mengchao Wang, Zhifeng Zhang, Hongyi Pan

“…Normalized Difference Vegetation Index (NDVI) is a key indicator to measure the health of ecosystems, which can effectively quantitatively reveal the spatial and temporal changes of ecological protection effects. …”
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Applicability Evaluation of China’s First Generation of Global Land Surface Reanalysis （CRA40/Land） Air Temperature and Precipitation Products in China Mainland by Caixia WANG, Anning HUANG, Peng ZHENG, Kai LIU, Min HOU

“…The applicability of first-generation of Chinese land surface reanalysis dataset （CRA40/Land） has been evaluated with the observations from 2231 national meteorological stations and further compared with the ERA5 and CFSR datasets in terms of the spatial and temporal variations of monthly air temperature and precipitation.Triangulation linear interpolation is used to interpolate grid data into stations， and station data into grid.The China mainland is divided into four regions according to the stations distribution and topographic features.The bias， standard deviation ratio， root mean square error （RMSE）， correlation coefficient and model skill of the 3 reanalysis datasets in four regions are computed to compare the accuracy and evaluate the application.The comparative results suggest that the CRA40， ERA5 and CFSR datasets can reproduce similar spatio-temporal distribution patterns of the monthly air temperature and precipitation with the observations in Mainland of China.In particular， they all show much better performance in the eastern China than in western China and in winter than in summer.For the monthly air temperature， the mean errors of the 3 reanalysis datasets are generally within 2 ℃， with slightly better performance for the CRA40 and ERA5 datasets.For the monthly precipitation， the errors of the 3 reanalysis datasets show large differences， the CRA40 performs the best and shows the highest applicability over Mainland of China among the 3 datasets.…”
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Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation by Refilwe F. Kai-Sikhakhane, Mary C. Scholes, Stuart J. Piketh, Jos van Geffen, Rebecca M. Garland, Henno Havenga, Robert J. Scholes

“…Yet, there is scant information on the current state and recent evolution of these atmospheric pollutants over a range of spatial and temporal scales, especially in Africa. …”
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Validation and sensitivity analysis of the FLEXPART model using the Kincaid experiment data by Huanting Li, Li Yang, Yinglu Cao, Xinpeng Li, Sheng Fang

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A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

“…The spatial feature extraction is enhanced by multi-scale feature extraction to capture fine details and broader patterns. …”
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Spatiotemporal evaluation of irrigation groundwater quality in Hungarian agricultural sites using hydrochemical and machine learning approaches by Musaab A. A. Mohammed, Norbert P. Szabó, Viktória Mikita, Péter Szűcs

“…The research integrates hydrochemical assessment with machine learning to enhance the spatiotemporal evaluation of irrigation water quality. Groundwater samples from the Debrecen area (2019–2024) were analyzed using Hierarchical Cluster Analysis (HCA), irrigation indices (SAR, Na%, IWQI), and Self-Organizing Maps (SOMs) to understand spatial and temporal patterns. …”
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Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers by Shenglei Wang, Zichen Mao, Bing Zhang, Xiaolong Yu, Fenqi Zhao, Xiaofei Song, Changyong Dou, Fangfang Zhang, Junsheng Li

“…The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.…”
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Compatibility Evaluation of Photovoltaic and Hydroelectric Power Generation Potential in the Beijiang River Basin Based on Climate Resources by Ren Yifang, Zhao Yanxia, Chen Sining, Sun Qing, Li Muyuan, Zhang Yi, Liu Li

“…Results indicate that, spatially, total solar radiation and runoff display a distribution pattern characterized by high in the middle and low on both sides and low in the north and high in the south, respectively. …”
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Performance Evaluation of Satellite Precipitation Products During Extreme Events—The Case of the Medicane Daniel in Thessaly, Greece by Dimitrios Katsanos, Adrianos Retalis, John Kalogiros, Basil E. Psiloglou, Nikolaos Roukounakis, Marios Anagnostou

“…The results indicate that, while satellite precipitation products demonstrate overall skill in capturing the broad-scale precipitation patterns associated with Medicane Daniel, discrepancies exist in estimating localized intense rainfall rates, particularly in convective cells within the cyclone’s core. …”
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Evaluating the effects of climate-induced heatwaves on the thermal dynamics and water quality of a deep reservoir in Portugal by Manuel Almeida, Senlin Zhu, Rita M. Cardoso, Pedro M.M. Soares, Pedro Coelho

“…A novel, in-depth approach is used to assess the spatial distribution of climate forcing effects across the reservoir's 2D space. …”
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