Literatura científica selecionada sobre o tema "Geo-Located time series"
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Artigos de revistas sobre o assunto "Geo-Located time series"
Guzmán-Vargas, L., A. Ramírez-Rojas e F. Angulo-Brown. "Multiscale entropy analysis of electroseismic time series". Natural Hazards and Earth System Sciences 8, n.º 4 (15 de agosto de 2008): 855–60. http://dx.doi.org/10.5194/nhess-8-855-2008.
Texto completo da fonteRödel, R., e T. Hoffmann. "Quantifying the efficiency of river regulation". Advances in Geosciences 5 (16 de dezembro de 2005): 75–82. http://dx.doi.org/10.5194/adgeo-5-75-2005.
Texto completo da fonteO'Dea, Annika, Katherine L. Brodie e Preston Hartzell. "Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner". Journal of Marine Science and Engineering 7, n.º 2 (7 de fevereiro de 2019): 37. http://dx.doi.org/10.3390/jmse7020037.
Texto completo da fonteKoltsida, Evgenia, e Andreas Kallioras. "Groundwater flow simulation through the application of the FREEWAT modeling platform". Journal of Hydroinformatics 21, n.º 5 (10 de julho de 2019): 812–33. http://dx.doi.org/10.2166/hydro.2019.040.
Texto completo da fonteZhu, Gaoyang, Muzhi Gao, Fanmin Kong e Kang Li. "Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering". Sensors 19, n.º 12 (19 de junho de 2019): 2754. http://dx.doi.org/10.3390/s19122754.
Texto completo da fonteClapuyt, François, Veerle Vanacker, Fritz Schlunegger e Kristof Van Oost. "Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models". Earth Surface Dynamics 5, n.º 4 (5 de dezembro de 2017): 791–806. http://dx.doi.org/10.5194/esurf-5-791-2017.
Texto completo da fonteMiller, Aaron, Inder Singh, Sarah Pilewski, Vladimir Petrovic e Philip M. Polgreen. "691. Real-Time Local Influenza Forecasting Using Smartphone-Connected Thermometer Readings". Open Forum Infectious Diseases 5, suppl_1 (novembro de 2018): S249. http://dx.doi.org/10.1093/ofid/ofy210.698.
Texto completo da fonteAbdur Rehman, Nabeel, Henrik Salje, Moritz U. G. Kraemer, Lakshminarayanan Subramanian, Umar Saif e Rumi Chunara. "Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan". PLOS Neglected Tropical Diseases 14, n.º 5 (11 de maio de 2020): e0008273. http://dx.doi.org/10.1371/journal.pntd.0008273.
Texto completo da fonteTrinh, Nghia Quoc, e Krishna Kanta Panthi. "Evaluation of Seismic Events Occurred after Filling and Drawdown of the Reservoir at Song Tranh 2 HPP in Vietnam". Hydro Nepal: Journal of Water, Energy and Environment 15 (22 de outubro de 2014): 16–20. http://dx.doi.org/10.3126/hn.v15i0.11285.
Texto completo da fonteBaude, Mike, e Burghard C. Meyer. "Changes of landscape structure and soil production function since the 18th century in north-west saxony". Journal of Environmental Geography 3, n.º 1-4 (1 de janeiro de 2010): 11–23. http://dx.doi.org/10.14232/jengeo-2010-43779.
Texto completo da fonteTeses / dissertações sobre o assunto "Geo-Located time series"
Zuo, Jingwei. "Apprentissage de représentations et prédiction pour des séries-temporelles inter-dépendantes". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG038.
Texto completo da fonteTime series is a common data type that has been applied to enormous real-life applications, such as financial analysis, medical diagnosis, environmental monitoring, astronomical discovery, etc. Due to its complex structure, time series raises several challenges in their data processing and mining. The representation of time series plays a key role in data mining tasks and machine learning algorithms for time series. Yet, a few methods consider the interrelation that may exist between different time series when building the representation. Moreover, the time series mining requires considering not only the time series' characteristics in terms of data complexity but also the concrete application scenarios where the data mining task is performed to build task-specific representations.In this thesis, we will study different time series representation approaches that can be used in various time series mining tasks, while capturing the relationships among them. We focus specifically on modeling the interrelations between different time series when building the representations, which can be the temporal relationship within each data source or the inter-variable relationship between various data sources. Accordingly, we study the time series collected from various application contexts under different forms. First, considering the temporal relationship between the observations, we learn the time series in a dynamic streaming context, i.e., time series stream, for which the time series data is continuously generated from the data source. Second, for the inter-variable relationship, we study the multivariate time series (MTS) with data collected from multiple data sources. Finally, we study the MTS in the Smart City context, when each data source is given a spatial position. The MTS then becomes a geo-located time series (GTS), for which the inter-variable relationship requires more modeling efforts with the external spatial information. Therefore, for each type of time series data collected from distinct contexts, the interrelations between the time series observations are emphasized differently, on the temporal or (and) variable axis.Apart from the data complexity from the interrelations, we study various machine learning tasks on time series in order to validate the learned representations. The high-level learning tasks studied in this thesis consist of time series classification, semi-supervised time series learning, and time series forecasting. We show how the learned representations connect with different time series learning tasks under distinct application contexts. More importantly, we conduct the interdisciplinary study on time series by leveraging real-life challenges in machine learning tasks, which allows for improving the learning model's performance and applying more complex time series scenarios.Concretely, for these time series learning tasks, our main research contributions are the following: (i) we propose a dynamic time series representation learning model in the streaming context, which considers both the characteristics of time series and the challenges in data streams. We claim and demonstrate that the Shapelet, a shape-based time series feature, is the best representation in such a dynamic context; (ii) we propose a semi-supervised model for representation learning in multivariate time series (MTS). The inter-variable relationship over multiple data sources is modeled in a real-life context, where the data annotations are limited; (iii) we design a geo-located time series (GTS) representation learning model for Smart City applications. We study specifically the traffic forecasting task, with a focus on the missing-value treatment within the forecasting algorithm
Trabalhos de conferências sobre o assunto "Geo-Located time series"
Reeves, Nigel, Gordon H. John e Bob Major. "Evaluation and Potential Remediation of the Industrial Norm Legacy in Liverpool". In ASME 2009 12th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2009. http://dx.doi.org/10.1115/icem2009-16096.
Texto completo da fonte