Predicting Urban Vitality at Regional Scales: A Deep Learning Approach to Modelling Population Density and Pedestrian Flows by Feifeng Jiang, Jun Ma

“…Applied to New York City, UVPN leverages diverse urban morphological features such as streetscape attributes and land use patterns to predict continuous vitality distributions. The model outperforms existing architectures, achieving reductions of 34.03% and 38.66% in mean squared error for population density and pedestrian flow predictions, respectively. …”
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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

“…In this paper, we introduce a novel deep learning-based model, adaptive-GCNLSTM (Ada-GCNLSTM). Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. …”
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Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
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Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins by HUANG Hua, MEI Le, ZHI Xiaobo, ZHANG Huiwang

“…Finally, the proposed method is validated using spiral spatial trajectories. Experimental results demonstrate that the error twin model improves prediction accuracy by 76. 04% compared to traditional mechanism models and achieves superior accuracy compared to similar neural network models. …”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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Predicting fine-scale downstream migratory movement of Atlantic salmon smolt (Salmo salar) in front of a hydropower plant by Kim M. Bærum, Ana T. Silva, Henrik Baktoft, Karl Ø. Gjelland, Finn Økland, Torbjørn Forseth

“…We present a spatially explicit individual-based model for predicting the movement of Atlantic salmon smolts in regulated rivers in Norway, parameterised for smolt movements in the River Mandal and the River Orkla. …”
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Modeling, Assessment, and Prediction of Carbon Storage in Hebei–Tianjin Coastal Wetlands by Liang Zhao, Hui Gao, Jintong Liu, Feng Wang, Tonggang Fu

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Multimodal neural network for enhanced protein stability prediction by integration of contact scores and spatial maps by G Gladstone Sigamani, P.M. Durai Raj Vincent

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Spatial prediction of landslide susceptibility using the data-mining algorithm (case study: Kamyaran county) by Osama Ashraf Mohammed, Hemn Kamal Hamaamin, Jwanro Azad, Sabri Rasooli, Houzhi Li

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Traffic flow prediction based on spatial-temporal multi factor fusion graph convolutional networks by Ying-Ting Chen, An Liu, Cheng Li, Shuang Li, Xiao Yang

“…Experimental results on four public real-world datasets prove the superiority of our model in terms of prediction performance and capturing the dynamic spatial-temporal correlation.…”
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Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices by Hao Xu, Hongfei Yin, Jia Liu, Lei Wang, Wenjie Feng, Hualu Song, Yangyang Fan, Kangkang Qi, Zhichao Liang, WenJie Li, Xiaohu Zhang, Rongjuan Zhang, Shuai Wang

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The spatial-temporal probability assessment for slope instability based on uncertainty of machine learning-based prediction by Tai-feng Li, Xian-pu Xiao, Rong-hui Yan, Kang Xie, You-ming Zhao, Qian-li Zhang, Mao-qin Che

“…As a result, the Bootstrap-GRU-Kriging (BGK) model for quantifying the spatial-temporal uncertainties of SCD prediction is developed. …”
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A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction by Yunyang Huang, Hongyu Yang, Zhen Yan

“…It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. …”
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SATF: a flight trajectory prediction method incorporating spatial awareness and time–frequency transformation by Jizhao Zhu, Zhuang Zhuang, Bing Han, Liang Zheng, Xinlong Pan

“…Although frequency-domain-based methods have achieved state-of-the-art performance for time series tasks, they fail to effectively capture the spatial dependencies inherent in flight trajectories with limited features, leading to suboptimal prediction performance. …”
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Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review by Tingfeng Zhang, Liang Zhao, Tingting Cui, Yi Zhou, Peifen Li, Chuqiao Luo, Junkang Wei, Hong Hu

“…It highlights the distinct advantages of spatial-temporal radiomics in predictive model development and examines potential correlations between imaging dynamics and gene expression profiles before and after NAC. …”
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Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory by Chaowei Wang, Chensheng Li, Cai Qin, Weidong Wang, Xiuhua Li

“…Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial–temporal coverage. …”
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GIS-based multi-criteria predictive modelling for geothermal energy exploration by Andongma Wanduku Tende, Mamidak Miner Iiiya, Serah Habu, Jiriko Nzeghi Gajere, Shekwonyadu Iyakwari, Mohammed Dahiru Aminu

“…The weighted sum model was then used to develop geothermal predictive maps while the accuracy of prediction was determined using the receiver operating characteristic/area under curve (ROC/AUC) analysis. …”
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Solar and Wind 24 H Sequenced Prediction Using L-Transform Component and Deep LSTM Learning in Representation of Spatial Pattern Correlation by Ladislav Zjavka

“…Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. …”
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The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale by Enrico Ruzzier, Seunghyun Lee, Pietro Tirozzi, Valerio Orioli, Andrea Di Giulio, Olivia Dondina, Luciano Bani

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An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle by Zhi Zhou, Xueling Wu, Bo Peng

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