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Статті в журналах з теми "Hydrologic Ensemble Prediction Systems"
Thirel, Guillaume, Fabienne Rousset-Regimbeau, Eric Martin, and Florence Habets. "On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions." Journal of Hydrometeorology 9, no. 6 (December 1, 2008): 1301–17. http://dx.doi.org/10.1175/2008jhm959.1.
Повний текст джерелаShrestha, Rajesh R., Markus A. Schnorbus, and Alex J. Cannon. "A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada." Journal of Hydrometeorology 16, no. 3 (May 27, 2015): 1273–92. http://dx.doi.org/10.1175/jhm-d-14-0167.1.
Повний текст джерелаVelázquez, J. A., F. Anctil, M. H. Ramos, and 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 (February 28, 2011): 33–42. http://dx.doi.org/10.5194/adgeo-29-33-2011.
Повний текст джерелаYuan, Xing, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield. "Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins." Bulletin of the American Meteorological Society 96, no. 11 (November 1, 2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.
Повний текст джерелаSaleh, F., V. Ramaswamy, N. Georgas, A. F. Blumberg, and 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, no. 1 (August 20, 2018): 166–86. http://dx.doi.org/10.2166/nh.2018.182.
Повний текст джерелаFranz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences 15, no. 11 (November 15, 2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.
Повний текст джерелаFranz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 30, 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
Повний текст джерелаCanli, Ekrem, Martin Mergili, Benni Thiebes, and Thomas Glade. "Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology." Natural Hazards and Earth System Sciences 18, no. 8 (August 16, 2018): 2183–202. http://dx.doi.org/10.5194/nhess-18-2183-2018.
Повний текст джерелаYe, Jinyin, Yuehong Shao, and 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.
Повний текст джерелаCarlberg, Bradley, Kristie Franz, and William Gallus. "A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting." Water 12, no. 12 (December 13, 2020): 3505. http://dx.doi.org/10.3390/w12123505.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаIn 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.
Повний текст джерелаVelázquez, 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.
Повний текст джерелаXu, 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.
Повний текст джерелаHydrological 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.
Повний текст джерелаDuncan, 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.
Повний текст джерелаCunningham, 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.
Повний текст джерелаThesis 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.
Повний текст джерелаThe 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.
Повний текст джерелаShrestha, 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.
Повний текст джерелаКниги з теми "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.
Знайти повний текст джерела(US), National Research Council. Report of a Workshop on Predictability & Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Знайти повний текст джерелаReport 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.
Повний текст джерелаWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies, and National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Знайти повний текст джерелаWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies, and National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Знайти повний текст джерелаWater Science and Technology Board, Board on Atmospheric Sciences and Climate, Committee on Hydrologic Science, Division on Earth and Life Studies, and National Research Council. Report of a Workshop on Predictability and Limits-To-Prediction in Hydrologic Systems. National Academies Press, 2002.
Знайти повний текст джерелаA 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.
Знайти повний текст джерелаЧастини книг з теми "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." In 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.
Повний текст джерелаPappenberger, 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." In 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.
Повний текст джерелаYang, Shu-Chih, Hsiang-Wen Cheng, Pin-Ying Wu, Zih-Mao Huang, and 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." In 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.
Повний текст джерелаTiwari, Stuti, and Namrata Dhanda. "Diabetes Prediction Using Ensemble Methods." In Smart Innovation, Systems and Technologies, 405–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_39.
Повний текст джерелаJindal, Rajni, Adil Ahmad, and Anshuman Aditya. "Ensemble Based-Cross Project Defect Prediction." In Smart Innovation, Systems and Technologies, 611–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3675-2_47.
Повний текст джерелаGabralla, Lubna A., Hela Mahersia, and Ajith Abraham. "Ensemble Neurocomputing Based Oil Price Prediction." In 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.
Повний текст джерелаAdhikari, Bikal, and Subarna Shakya. "Heart Disease Prediction Using Ensemble Model." In Lecture Notes in Networks and Systems, 857–68. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7657-4_69.
Повний текст джерелаWadghiri, Mohamed Zaim, Ali Idri, and Touria El Idrissi. "Ensemble Regression for Blood Glucose Prediction." In 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.
Повний текст джерелаGovinda, K., R. Rajkumar, and Jolly Masih. "Bitcoin Prediction Using Ensemble Modelling." In 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.
Повний текст джерелаMarndi, Ashapurna, and G. K. Patra. "Multidimensional Ensemble LSTM for Wind Speed Prediction." In Communication and Intelligent Systems, 595–606. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1089-9_47.
Повний текст джерелаТези доповідей конференцій з теми "Hydrologic Ensemble Prediction Systems"
Madadgar, Shahrbanou, and Hamid Moradkhani. "Improving the Ensemble Streamflow Prediction by Adjusting Hydrologic Ensemble Traces." In World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)392.
Повний текст джерелаAdams, Thomas, and Joseph Ostrowski. "Short Lead-Time Hydrologic Ensemble Forecasts from Numerical Weather Prediction Model Ensembles." In World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)237.
Повний текст джерелаPacheco, Sheilla Ann B. "Breast Cancer Prediction using Ensemble Technique." In 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2022. http://dx.doi.org/10.1109/icccis56430.2022.10037589.
Повний текст джерелаBajaj, Madhvan, Priyanshu Rawat, Chandradeep Bhatt, Rahul Chauhan, and Teekam Singh. "Heart Disease Prediction using Ensemble ML." In 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104770.
Повний текст джерелаQiu, Xiaokang, Yuan Zuo, and Guannan Liu. "ETCF: An Ensemble Model for CTR Prediction." In 2018 15th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2018. http://dx.doi.org/10.1109/icsssm.2018.8465044.
Повний текст джерелаAlthaph, B., S. V. N. Sreenivasu, and D. Venkata Reddy. "Student Performance Analysis with Ensemble Progressive Prediction." In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2023. http://dx.doi.org/10.1109/icssit55814.2023.10060910.
Повний текст джерелаWang, Jian, Jin Guo, Yueying Li, Ran Hao, and Hongjun Wang. "Unsupervised clustering ensemble for traffic level prediction." In Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022). WORLD SCIENTIFIC, 2023. http://dx.doi.org/10.1142/9789811269264_0066.
Повний текст джерелаSanthosh, M., M. Dharani Sai, and Sanober Mirza. "Ensemble deep learning model for wind speed prediction." In 2020 21st National Power Systems Conference (NPSC). IEEE, 2020. http://dx.doi.org/10.1109/npsc49263.2020.9331836.
Повний текст джерелаAdegoke, Vincent F., Daqing Chen, Ebad Banissi, and Safia Barikzai. "Prediction of breast cancer survivability using ensemble algorithms." In 2017 International Conference on Smart Systems and Technologies (SST). IEEE, 2017. http://dx.doi.org/10.1109/sst.2017.8188699.
Повний текст джерелаXiang, Chengguan, Mei Chen, and Hanhu Wang. "An Ensemble Method for Medicine Best Selling Prediction." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.245.
Повний текст джерела