Artigos de revistas sobre o tema "Classification and spatiotemporal forecasting"
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Wang, Guosong, Xidong Wang, Xinrong Wu, Kexiu Liu, Yiquan Qi, Chunjian Sun e Hongli Fu. "A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting". Journal of Atmospheric and Oceanic Technology 39, n.º 3 (março de 2022): 285–301. http://dx.doi.org/10.1175/jtech-d-21-0043.1.
Texto completo da fontePlain, M. B., B. Minasny, A. B. McBratney e R. W. Vervoort. "Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data". Hydrology and Earth System Sciences Discussions 5, n.º 3 (14 de maio de 2008): 1159–89. http://dx.doi.org/10.5194/hessd-5-1159-2008.
Texto completo da fonteJiang, Hongxun, Xiaotong Wang e Caihong Sun. "Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features". Atmosphere 13, n.º 11 (23 de outubro de 2022): 1744. http://dx.doi.org/10.3390/atmos13111744.
Texto completo da fonteYusro, Muhammad, e Isnaini Nurisusilawati. "Forecasting Approach to Investigate Dynamic Growth of Organoid within 3D Matrix for Distinct Perspective". Journal of Biomimetics, Biomaterials and Biomedical Engineering 59 (14 de fevereiro de 2023): 107–17. http://dx.doi.org/10.4028/p-99od29.
Texto completo da fonteAkarsu, Osman Nuri. "A Bibliometric Review of Earthquake and Machine Learning Research". January 2024 5, n.º 1 (1 de abril de 2024): 1–10. http://dx.doi.org/10.36937/cebel.2024.1908.
Texto completo da fonteRotti, Sumanth, e Petrus C. Martens. "Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog". Astrophysical Journal Supplement Series 267, n.º 2 (1 de agosto de 2023): 40. http://dx.doi.org/10.3847/1538-4365/acdace.
Texto completo da fonteHushtan, Tetiana, e Anatoliy Kolodiychuk. "DEFINING CONDITIONS FOR INCREASING INNOVATION ACTIVITY IN THE INDUSTRIAL COMPLEX: ESSENCE, SYSTEMATIZATION, IDENTIFICATION". Baltic Journal of Economic Studies 7, n.º 4 (27 de setembro de 2021): 54–62. http://dx.doi.org/10.30525/2256-0742/2021-7-4-54-62.
Texto completo da fonteZhang, Yi, Fang Liu, Sheng Yue, Yuxuan Li e Qianwei Dong. "Accident Detection and Flow Prediction for Connected and Automated Transport Systems". Journal of Advanced Transportation 2023 (17 de abril de 2023): 1–9. http://dx.doi.org/10.1155/2023/5041509.
Texto completo da fonteKhokhlov, V., О. Umanska e I. Deriabina. "Objective classification of atmospheric processes for the East European region". Physical Geography and Geomorphology 90, n.º 2 (2018): 84–90. http://dx.doi.org/10.17721/phgg.2018.2.10.
Texto completo da fonteFossa, Manuel, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel e Jean-Philippe Vidal. "Spatiotemporal and cross-scale interactions in hydroclimate variability: a case-study in France". Hydrology and Earth System Sciences 25, n.º 11 (4 de novembro de 2021): 5683–702. http://dx.doi.org/10.5194/hess-25-5683-2021.
Texto completo da fonteZhang, Tuantuan, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu e Binquan Li. "Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks". Hydrology and Earth System Sciences 27, n.º 10 (22 de maio de 2023): 1945–60. http://dx.doi.org/10.5194/hess-27-1945-2023.
Texto completo da fonteAbdulrazzaq, Zaidoon T., Raghad H. Hasan e Nadia A. Aziz. "Integrated TRMM Data and Standardized Precipitation Index to Monitor the Meteorological Drought". Civil Engineering Journal 5, n.º 7 (21 de julho de 2019): 1590–98. http://dx.doi.org/10.28991/cej-2019-03091355.
Texto completo da fonteGel, Yulia R. "Comparative Analysis of the Local Observation-Based (LOB) Method and the Nonparametric Regression-Based Method for Gridded Bias Correction in Mesoscale Weather Forecasting". Weather and Forecasting 22, n.º 6 (1 de dezembro de 2007): 1243–56. http://dx.doi.org/10.1175/2007waf2006046.1.
Texto completo da fonteLima, Carlos H. R., Amir AghaKouchak e Upmanu Lall. "Classification of mechanisms, climatic context, areal scaling, and synchronization of floods: the hydroclimatology of floods in the Upper Paraná River basin, Brazil". Earth System Dynamics 8, n.º 4 (1 de dezembro de 2017): 1071–91. http://dx.doi.org/10.5194/esd-8-1071-2017.
Texto completo da fonteLiu, Qian, Yun Li, Manzhu Yu, Long S. Chiu, Xianjun Hao, Daniel Q. Duffy e Chaowei Yang. "Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images". Remote Sensing 11, n.º 21 (30 de outubro de 2019): 2555. http://dx.doi.org/10.3390/rs11212555.
Texto completo da fonteDillon, James, Christopher Donahue, Evan Schehrer, Karl Birkeland e Kevin Hammonds. "Mapping surface hoar from near-infrared texture in a laboratory". Cryosphere 18, n.º 5 (24 de maio de 2024): 2557–82. http://dx.doi.org/10.5194/tc-18-2557-2024.
Texto completo da fonteBogner, Konrad, Annie Y. Y. Chang, Luzi Bernhard, Massimiliano Zappa, Samuel Monhart e Christoph Spirig. "Tercile Forecasts for Extending the Horizon of Skillful Hydrological Predictions". Journal of Hydrometeorology 23, n.º 4 (abril de 2022): 521–39. http://dx.doi.org/10.1175/jhm-d-21-0020.1.
Texto completo da fonteRegasa, Motuma Shiferaw, Michael Nones e Dereje Adeba. "A Review on Land Use and Land Cover Change in Ethiopian Basins". Land 10, n.º 6 (1 de junho de 2021): 585. http://dx.doi.org/10.3390/land10060585.
Texto completo da fonteForoushani, Mansour A., Christian Opp, Michael Groll e Amirhossein Nikfal. "Evaluation of WRF-Chem Predictions for Dust Deposition in Southwestern Iran". Atmosphere 11, n.º 7 (17 de julho de 2020): 757. http://dx.doi.org/10.3390/atmos11070757.
Texto completo da fonteRios Gaona, M. F., A. Overeem, H. Leijnse e R. Uijlenhoet. "Sources of uncertainty in rainfall maps from cellular communication networks". Hydrology and Earth System Sciences Discussions 12, n.º 3 (25 de março de 2015): 3289–317. http://dx.doi.org/10.5194/hessd-12-3289-2015.
Texto completo da fonteRios Gaona, M. F., A. Overeem, H. Leijnse e R. Uijlenhoet. "Measurement and interpolation uncertainties in rainfall maps from cellular communication networks". Hydrology and Earth System Sciences 19, n.º 8 (14 de agosto de 2015): 3571–84. http://dx.doi.org/10.5194/hess-19-3571-2015.
Texto completo da fonteLi, Cheng, Weimin Zheng e Peng Ge. "Tourism demand forecasting with spatiotemporal features". Annals of Tourism Research 94 (maio de 2022): 103384. http://dx.doi.org/10.1016/j.annals.2022.103384.
Texto completo da fonteMurr, Georges, e Saliya Coulibaly. "Machine Learning-assisted spatiotemporal chaos forecasting". EPJ Web of Conferences 287 (2023): 13002. http://dx.doi.org/10.1051/epjconf/202328713002.
Texto completo da fonteLin, Xu, Hongyue Wang, Qingqing Zhang, Chaolong Yao, Changxin Chen, Lin Cheng e Zhaoxiong Li. "A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting". Remote Sensing 14, n.º 7 (2 de abril de 2022): 1717. http://dx.doi.org/10.3390/rs14071717.
Texto completo da fonteOliveira, Mariana, Luís Torgo e Vítor Santos Costa. "Evaluation Procedures for Forecasting with Spatiotemporal Data". Mathematics 9, n.º 6 (23 de março de 2021): 691. http://dx.doi.org/10.3390/math9060691.
Texto completo da fontePavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling". Sensors 20, n.º 23 (4 de dezembro de 2020): 6931. http://dx.doi.org/10.3390/s20236931.
Texto completo da fonteMuñoz-Organero, Mario, e Paula Queipo-Álvarez. "Deep Spatiotemporal Model for COVID-19 Forecasting". Sensors 22, n.º 9 (5 de maio de 2022): 3519. http://dx.doi.org/10.3390/s22093519.
Texto completo da fonte., V. Nourani, A. A. Moghaddam ., A. O. Nadiri . e V. P. Singh . "Forecasting Spatiotemporal Water Levels of Tabriz Aquifer". Trends in Applied Sciences Research 3, n.º 4 (1 de abril de 2008): 319–29. http://dx.doi.org/10.3923/tasr.2008.319.329.
Texto completo da fonteLópez, Cristóbal, Alberto Álvarez e Emilio Hernández-García. "Forecasting Confined Spatiotemporal Chaos with Genetic Algorithms". Physical Review Letters 85, n.º 11 (11 de setembro de 2000): 2300–2303. http://dx.doi.org/10.1103/physrevlett.85.2300.
Texto completo da fonteErmagun, Alireza, e David Levinson. "Spatiotemporal traffic forecasting: review and proposed directions". Transport Reviews 38, n.º 6 (6 de março de 2018): 786–814. http://dx.doi.org/10.1080/01441647.2018.1442887.
Texto completo da fonteLi, Zhenxin, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun e Ge Chen. "PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting". ISPRS International Journal of Geo-Information 12, n.º 6 (16 de junho de 2023): 241. http://dx.doi.org/10.3390/ijgi12060241.
Texto completo da fonteRapantzikos, Konstantinos, Nicolas Tsapatsoulis, Yannis Avrithis e Stefanos Kollias. "Spatiotemporal saliency for video classification". Signal Processing: Image Communication 24, n.º 7 (agosto de 2009): 557–71. http://dx.doi.org/10.1016/j.image.2009.03.002.
Texto completo da fonteLiu, Gang, Silu He, Xing Han, Qinyao Luo, Ronghua Du, Xinsha Fu e Ling Zhao. "Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting". Symmetry 15, n.º 11 (31 de outubro de 2023): 2002. http://dx.doi.org/10.3390/sym15112002.
Texto completo da fonteCheng, Yong, Haifeng Qu, Jun Wang, Kun Qian, Wei Li, Ling Yang, Xiaodong Han e Min Liu. "A Radar Echo Extrapolation Model Based on a Dual-Branch Encoder–Decoder and Spatiotemporal GRU". Atmosphere 15, n.º 1 (14 de janeiro de 2024): 104. http://dx.doi.org/10.3390/atmos15010104.
Texto completo da fonteSajan, Bhartendu, Varun Narayan Mishra, Shruti Kanga, Gowhar Meraj, Suraj Kumar Singh e Pankaj Kumar. "Cellular Automata-Based Artificial Neural Network Model for Assessing Past, Present, and Future Land Use/Land Cover Dynamics". Agronomy 12, n.º 11 (7 de novembro de 2022): 2772. http://dx.doi.org/10.3390/agronomy12112772.
Texto completo da fonteHeinecke, G., A. A. Syntetos e W. Wang. "Forecasting-based SKU classification". International Journal of Production Economics 143, n.º 2 (junho de 2013): 455–62. http://dx.doi.org/10.1016/j.ijpe.2011.11.020.
Texto completo da fontePavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting". Algorithms 13, n.º 2 (13 de fevereiro de 2020): 39. http://dx.doi.org/10.3390/a13020039.
Texto completo da fonteKolidakis, Stylianos Z., Kornilia Maria A. Kotoula e George N. Botzoris. "School Mode Choice Classification Model Exploitation Though Artificial Intelligence Classification Application". Mathematical Modelling of Engineering Problems 9, n.º 6 (31 de dezembro de 2022): 1441–50. http://dx.doi.org/10.18280/mmep.090601.
Texto completo da fonteXiong, Liyan, Weihua Ding, Xiaohui Huang e Weichun Huang. "CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting". Mathematical Problems in Engineering 2022 (11 de julho de 2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.
Texto completo da fontePavlyuk, Dmitry. "Spatiotemporal cross-validation of urban traffic forecasting models". Transportation Research Procedia 52 (2021): 179–86. http://dx.doi.org/10.1016/j.trpro.2021.01.020.
Texto completo da fonteKaboudan, M. A. "SPATIOTEMPORAL FORECASTING OF HOME PRICES: A GIS APPLICATION". IFAC Proceedings Volumes 38, n.º 1 (2005): 95–99. http://dx.doi.org/10.3182/20050703-6-cz-1902.02251.
Texto completo da fonteDirector, Hannah M., Adrian E. Raftery e Cecilia M. Bitz. "Improved Sea Ice Forecasting through Spatiotemporal Bias Correction". Journal of Climate 30, n.º 23 (dezembro de 2017): 9493–510. http://dx.doi.org/10.1175/jcli-d-17-0185.1.
Texto completo da fontePrestemon, Jeffrey P., María L. Chas-Amil, Julia M. Touza e Scott L. Goodrick. "Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations". International Journal of Wildland Fire 21, n.º 6 (2012): 743. http://dx.doi.org/10.1071/wf11049.
Texto completo da fonteChai, Songjian, Zhao Xu, Youwei Jia e Wai Kin Wong. "A Robust Spatiotemporal Forecasting Framework for Photovoltaic Generation". IEEE Transactions on Smart Grid 11, n.º 6 (novembro de 2020): 5370–82. http://dx.doi.org/10.1109/tsg.2020.3006085.
Texto completo da fonteLenzi, Amanda, e Marc G. Genton. "Spatiotemporal probabilistic wind vector forecasting over Saudi Arabia". Annals of Applied Statistics 14, n.º 3 (setembro de 2020): 1359–78. http://dx.doi.org/10.1214/20-aoas1347.
Texto completo da fonteYou, Yujie, Le Zhang, Peng Tao, Suran Liu e Luonan Chen. "Spatiotemporal Transformer Neural Network for Time-Series Forecasting". Entropy 24, n.º 11 (14 de novembro de 2022): 1651. http://dx.doi.org/10.3390/e24111651.
Texto completo da fonteJiao, Xiaoying, Gang Li e Jason Li Chen. "Forecasting international tourism demand: a local spatiotemporal model". Annals of Tourism Research 83 (julho de 2020): 102937. http://dx.doi.org/10.1016/j.annals.2020.102937.
Texto completo da fonteAbirami, S., e P. Chitra. "Regional air quality forecasting using spatiotemporal deep learning". Journal of Cleaner Production 283 (fevereiro de 2021): 125341. http://dx.doi.org/10.1016/j.jclepro.2020.125341.
Texto completo da fonteLi, Yung-Chen, Hsiao-Yun Huang, Nan-Ping Yang e Yi-Hung Kung. "Stock Market Forecasting Based on Spatiotemporal Deep Learning". Entropy 25, n.º 9 (12 de setembro de 2023): 1326. http://dx.doi.org/10.3390/e25091326.
Texto completo da fonteYue, Aming, e Wenhua Wu. "STGWN: Enhanced spatiotemporal wave forecasting using multiscale features". Applied Ocean Research 145 (abril de 2024): 103923. http://dx.doi.org/10.1016/j.apor.2024.103923.
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