Literatura académica sobre el tema "Hydrologic Ensemble Prediction Systems"
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Artículos de revistas sobre el tema "Hydrologic Ensemble Prediction Systems"
Thirel, Guillaume, Fabienne Rousset-Regimbeau, Eric Martin y Florence Habets. "On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions". Journal of Hydrometeorology 9, n.º 6 (1 de diciembre de 2008): 1301–17. http://dx.doi.org/10.1175/2008jhm959.1.
Texto completoShrestha, Rajesh R., Markus A. Schnorbus y Alex J. Cannon. "A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada". Journal of Hydrometeorology 16, n.º 3 (27 de mayo de 2015): 1273–92. http://dx.doi.org/10.1175/jhm-d-14-0167.1.
Texto completoVelázquez, J. A., F. Anctil, M. H. Ramos y C. Perrin. "Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures". Advances in Geosciences 29 (28 de febrero de 2011): 33–42. http://dx.doi.org/10.5194/adgeo-29-33-2011.
Texto completoYuan, Xing, Joshua K. Roundy, Eric F. Wood y Justin Sheffield. "Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins". Bulletin of the American Meteorological Society 96, n.º 11 (1 de noviembre de 2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.
Texto completoSaleh, F., V. Ramaswamy, N. Georgas, A. F. Blumberg y J. Pullen. "Inter-comparison between retrospective ensemble streamflow forecasts using meteorological inputs from ECMWF and NOAA/ESRL in the Hudson River sub-basins during Hurricane Irene (2011)". Hydrology Research 50, n.º 1 (20 de agosto de 2018): 166–86. http://dx.doi.org/10.2166/nh.2018.182.
Texto completoFranz, K. J. y T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community". Hydrology and Earth System Sciences 15, n.º 11 (15 de noviembre de 2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.
Texto completoFranz, K. J. y T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community". Hydrology and Earth System Sciences Discussions 8, n.º 2 (30 de marzo de 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
Texto completoCanli, Ekrem, Martin Mergili, Benni Thiebes y Thomas Glade. "Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology". Natural Hazards and Earth System Sciences 18, n.º 8 (16 de agosto de 2018): 2183–202. http://dx.doi.org/10.5194/nhess-18-2183-2018.
Texto completoYe, Jinyin, Yuehong Shao y Zhijia Li. "Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast". Advances in Meteorology 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9129734.
Texto completoCarlberg, Bradley, Kristie Franz y William Gallus. "A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting". Water 12, n.º 12 (13 de diciembre de 2020): 3505. http://dx.doi.org/10.3390/w12123505.
Texto completoTesis sobre el tema "Hydrologic Ensemble Prediction Systems"
Brochero, Darwin. "Hydroinformatics and diversity in hydrological ensemble prediction systems". Thesis, Université Laval, 2013. http://www.theses.ulaval.ca/2013/29908/29908.pdf.
Texto completoIn this thesis, we tackle the problem of streamflow probabilistic forecasting from two different perspectives based on multiple hydrological models collaboration (diversity). The first one favours a hybrid approach for the evaluation of multiple global hydrological models and tools of machine learning for predictors selection, while the second one constructs Artificial Neural Network (ANN) ensembles, forcing diversity within. This thesis is based on the concept of diversity for developing different methodologies around two complementary problems. The first one focused on simplifying, via members selection, a complex Hydrological Ensemble Prediction System (HEPS) that has 800 daily forecast scenarios originating from the combination of 50 meteorological precipitation members and 16 global hydrological models. We explore in depth four techniques: Linear Correlation Elimination, Mutual Information, Backward Greedy Selection, and Nondominated Sorting Genetic Algorithm II (NSGA-II). We propose the optimal hydrological model participation concept that identifies the number of meteorological representative members to propagate into each hydrological model in the simplified HEPS scheme. The second problem consists in the stratified selection of data patterns that are used for training an ANN ensemble or stack. For instance, taken from the database of the second and third MOdel Parameter Estimation eXperiment (MOPEX) workshops, we promoted an ANN prediction stack in which each predictor is trained on input spaces defined by the Input Variable Selection application on different stratified sub-samples. In summary, we demonstrated that implicit diversity in the configuration of a HEPS is efficient in the search for a HEPS of high performance.
Velazquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting". Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27792/27792.pdf.
Texto completoVelázquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting". Doctoral thesis, Université Laval, 2010. http://hdl.handle.net/20.500.11794/22245.
Texto completoXu, Jing. "Hydrological post-processing of streamflow forecasts issued from single-model and multimodel ensemble prediction systems". Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69503.
Texto completoHydrological simulations and forecasts are subject to various sources of uncertainties. Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, necessarily imperfect hydrologic modeling, and the inconsistent stationnarity assumption in a changing environment. Ensemble forecasting is a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. Thiboult et al. (2016) constructed a 50,000-member great ensemble that accounts for meteorological forcing uncertainty, initial conditions uncertainty, and structural uncertainty. This large ensemble can also be separated into sub-components to untangle the three main sources of uncertainties mentioned above. In asimilar experiment, another multimodel hydrological ensemble forecasting system implemented for different catchments was produced by Emixi Valdez. However,in the latter case, model outputs were simply pooled together, considering the members equiprobable. Although multimodel hydrological ensemble forecasting systems can be considered very comprehensive, they can still underestimate the total uncertainty. For instance, the meteorological forecasts in there search of Thiboult et al. (2016) were pre-tested on some watersheds. It was found out that the forecasting performance of data assimilation fades away quickly as the lead time progresses. In addition, operational forecasts users may not able to perfectly utilize all the forecasting tools (i.e., meteorological ensemble forcing, data assimilation, and multimodel) jointly. Therefore, there is still room for improvement to enhance the forecasting skill of such systems through proper statistical post-processing.The global objective of this research is to explore the proper use and predictive skill of various statistical post-processing algorithms by testing them on single-model and multimodel ensemble stream flow forecasts. First, we tested the post-processing skills of Affine kernel dressing (AKD) and Non-dominated sorting genetic algorithm II (NSGA-II) over single-model H-EPSs. Those two methods are theoretically/technically distinct yet are both non-parametric. They do not require the raw ensemble members to follow a specific parametric distribution.AKD-transformed ensembles and the Pareto fronts generated with NSGA-II demonstrated the superiority of post-processed ensembles compared to raw ensembles. Both methods where efficient at eliminating biases and maintaining a proper dispersion for all forecasting horizons. For multimodel ensembles, two post-processors, namely Bayesian model averaging (BMA) and the integrated copula-BMA, are compared for deriving a pertinent joint predictive distribution of daily streamflow forecasts issued by five different single-model hydrological ensemble prediction systems (H-EPSs). BMA assign weights to different models. Forecasts from all models are then combined to generate more skillful and reliable probabilistic forecasts. BMA weights quantify the level of confidence one can have regarding each candidate hydrological model and lead to a predictive probabilistic density function (PDF) containing information about uncertainty. BMA improves the overall quality of forecasts mainly by maintaining the ensemble dispersion with the lead time. It also improves the reliability and skill of multimodel systems that only include two sources of uncertainties compared to the 50,000-member great ensemble from Thiboult et al (2016). Furthermore, Thiboult et al. (2016) showed that the meteorological forecasts they used were biased and unreliable on some catchments. BMA improves the accuracy and reliability of the hydrological forecasts in that case as well.However, BMA suffers from limitations pertaining to its conditional probability density functions (PDFs), which must follow a known parametric distribution form (e.g., normal, gamma). On the contrary, Copula-BMA predictive model fully relaxes this constraint and also eliminates the power transformation step. In this study, eleven univariate marginal distributions and six copula functions are explored in a Copula-BMA framework for comprehensively reflecting the dependence structure between pairs of forecasted and observed streamflow. Results demonstrate the superiority of the Copula-BMAcompared to BMA in eliminating biases and maintaining an appropriate ensemble dispersion for all lead-times.
Wood, Andrew W. "Using climate model ensemble forecasts for seasonal hydrologic prediction /". Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10205.
Texto completoDuncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.
Texto completoCunningham, Jeffrey G. "Applying ensemble prediction systems to Department of Defense operations". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FCunningham.pdf.
Texto completoThesis Advisor(s): Carlyle H. Wash, Patrick A. Harr. "March 2006." Includes bibliographical references (p. 129). Also available online.
Pearman, Douglas W. "Evaluating tropical cyclone forecast track uncertainty using a grand ensemble of ensemble prediction systems". Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5465.
Texto completoThe skill of a combined grand ensemble (GE), which is constructed from three operational global ensemble prediction systems (EPS), is evaluated with respect to the probability forecast of a tropical cyclone (TC) being within a specified area. Anisotropic probability ellipses are defined from the GE to contain 68% of the ensemble members. Forecast reliability is based on whether the forecast verifying position is within the ellipse. A sharpness parameter is based on the size of the GE-based probability ellipse relative to other operational forecast probability ellipses. For the 2010 Atlantic TC season, results indicate that the GE ellipses exhibit a high degree of reliability whereas the operational probability circle tends to be over-dispersive. Additionally, the GE ellipse tends to be sharper than the operational product for forecast intervals beyond 48 hours. The size and shape of the GE ellipses varied with TC track types, which suggests that information about the physics of the flow-dependent system is retained whereas isotropic probability ellipses may not reflect variability associated with track type. It is concluded that the GE probability ellipse demonstrates utility for combined EPS to enhance probabilistic forecasts for use as TC-related decision aids, as there is a potential for reducing the sizes of warning areas.
Sağlam, Şenay Yaşar. "The role of confidence and diversity in dynamic ensemble class prediction systems". Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1940.
Texto completoShrestha, Rajesh Raj. "River flood prediction systems : towards complementary hydrodynamic, hydrological and data driven models with uncertainty analysis /". Karlsruhe : Institut für Wasser und Gewässerentwicklung Universität Karlsruhe (TH), 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014799092&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Texto completoLibros sobre el tema "Hydrologic Ensemble Prediction Systems"
Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems (1st 2000 Boulder, Colo.). Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems. Washington, D.C: National Academy Press, 2002.
Buscar texto completo(US), National Research Council. Report of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Buscar texto completoReport of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems. Washington, D.C.: National Academies Press, 2002. http://dx.doi.org/10.17226/10337.
Texto completoWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies y National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Buscar texto completoWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies y National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Buscar texto completoWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies y National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Buscar texto completoA geographic information system/hydrologic modeling graphical user interface for flood prediction and assessment. [Champaign, Ill.]: US Army Corps of Engineers, Construction Engineers Research Laboritories, 1994.
Buscar texto completoCapítulos de libros sobre el tema "Hydrologic Ensemble Prediction Systems"
Pappenberger, F., T. C. Pagano, J. D. Brown, L. Alfieri, D. A. Lavers, L. Berthet, F. Bressand et al. "Hydrological Ensemble Prediction Systems Around the Globe". En Handbook of Hydrometeorological Ensemble Forecasting, 1–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-40457-3_47-1.
Texto completoPappenberger, Florian, Thomas C. Pagano, J. D. Brown, Lorenzo Alfieri, D. A. Lavers, L. Berthet, F. Bressand et al. "Hydrological Ensemble Prediction Systems Around the Globe". En Handbook of Hydrometeorological Ensemble Forecasting, 1187–221. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-39925-1_47.
Texto completoYang, Shu-Chih, Hsiang-Wen Cheng, Pin-Ying Wu, Zih-Mao Huang y Chih-Chien Tsai. "Convective-Scale Data Assimilation and Precipitation Prediction with a Local Ensemble Transform Kalman Filter Radar Assimilation System Over Complex Terrain: A Thorough Investigation with the Heavy Rainfall in Taiwan on 16 June 2008". En Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), 543–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77722-7_21.
Texto completoTiwari, Stuti y Namrata Dhanda. "Diabetes Prediction Using Ensemble Methods". En Smart Innovation, Systems and Technologies, 405–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_39.
Texto completoJindal, Rajni, Adil Ahmad y Anshuman Aditya. "Ensemble Based-Cross Project Defect Prediction". En Smart Innovation, Systems and Technologies, 611–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3675-2_47.
Texto completoGabralla, Lubna A., Hela Mahersia y Ajith Abraham. "Ensemble Neurocomputing Based Oil Price Prediction". En Advances in Intelligent Systems and Computing, 293–302. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13572-4_24.
Texto completoAdhikari, Bikal y Subarna Shakya. "Heart Disease Prediction Using Ensemble Model". En Lecture Notes in Networks and Systems, 857–68. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7657-4_69.
Texto completoWadghiri, Mohamed Zaim, Ali Idri y Touria El Idrissi. "Ensemble Regression for Blood Glucose Prediction". En Advances in Intelligent Systems and Computing, 544–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72657-7_52.
Texto completoGovinda, K., R. Rajkumar y Jolly Masih. "Bitcoin Prediction Using Ensemble Modelling". En Artificial Intelligence Systems and the Internet of Things in the Digital Era, 162–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77246-8_16.
Texto completoMarndi, Ashapurna y G. K. Patra. "Multidimensional Ensemble LSTM for Wind Speed Prediction". En Communication and Intelligent Systems, 595–606. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1089-9_47.
Texto completoActas de conferencias sobre el tema "Hydrologic Ensemble Prediction Systems"
Madadgar, Shahrbanou y Hamid Moradkhani. "Improving the Ensemble Streamflow Prediction by Adjusting Hydrologic Ensemble Traces". En World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)392.
Texto completoAdams, Thomas y Joseph Ostrowski. "Short Lead-Time Hydrologic Ensemble Forecasts from Numerical Weather Prediction Model Ensembles". En World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)237.
Texto completoPacheco, Sheilla Ann B. "Breast Cancer Prediction using Ensemble Technique". En 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2022. http://dx.doi.org/10.1109/icccis56430.2022.10037589.
Texto completoBajaj, Madhvan, Priyanshu Rawat, Chandradeep Bhatt, Rahul Chauhan y Teekam Singh. "Heart Disease Prediction using Ensemble ML". En 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104770.
Texto completoQiu, Xiaokang, Yuan Zuo y Guannan Liu. "ETCF: An Ensemble Model for CTR Prediction". En 2018 15th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2018. http://dx.doi.org/10.1109/icsssm.2018.8465044.
Texto completoAlthaph, B., S. V. N. Sreenivasu y D. Venkata Reddy. "Student Performance Analysis with Ensemble Progressive Prediction". En 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2023. http://dx.doi.org/10.1109/icssit55814.2023.10060910.
Texto completoWang, Jian, Jin Guo, Yueying Li, Ran Hao y Hongjun Wang. "Unsupervised clustering ensemble for traffic level prediction". En Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022). WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811269264_0066.
Texto completoSanthosh, M., M. Dharani Sai y Sanober Mirza. "Ensemble deep learning model for wind speed prediction". En 2020 21st National Power Systems Conference (NPSC). IEEE, 2020. http://dx.doi.org/10.1109/npsc49263.2020.9331836.
Texto completoAdegoke, Vincent F., Daqing Chen, Ebad Banissi y Safia Barikzai. "Prediction of breast cancer survivability using ensemble algorithms". En 2017 International Conference on Smart Systems and Technologies (SST). IEEE, 2017. http://dx.doi.org/10.1109/sst.2017.8188699.
Texto completoXiang, Chengguan, Mei Chen y Hanhu Wang. "An Ensemble Method for Medicine Best Selling Prediction". En 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.245.
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