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Статті в журналах з теми "Flood forecasting Statistical methods"
Mandryk, Oleg, Andriy Oliynyk, Roman Mykhailyuk, and Lidiia Feshanych. "Flood Development Process Forecasting Based on Water Resources Statistical Data." Grassroots Journal of Natural Resources 4, no. 2 (May 30, 2021): 65–76. http://dx.doi.org/10.33002/nr2581.6853.040205.
Повний текст джерелаMargaryan, Varduhi, Ekaterina Gaidukova, and Gennady Tsibulskii. "Methods for long-term forecasting of water availability in spring floods (r. Arpa – p. Jermuk)." E3S Web of Conferences 333 (2021): 02007. http://dx.doi.org/10.1051/e3sconf/202133302007.
Повний текст джерелаŠaur, David, and Lukáš Pavlík. "Comparison of accuracy of forecasting methods of convective precipitation." MATEC Web of Conferences 210 (2018): 04035. http://dx.doi.org/10.1051/matecconf/201821004035.
Повний текст джерелаAtashi, Vida, Hamed Taheri Gorji, Seyed Mojtaba Shahabi, Ramtin Kardan, and Yeo Howe Lim. "Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North." Water 14, no. 12 (June 20, 2022): 1971. http://dx.doi.org/10.3390/w14121971.
Повний текст джерелаWu, Jian, Haixing Liu, Guozhen Wei, Tianyu Song, Chi Zhang, and Huicheng Zhou. "Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment." Water 11, no. 7 (June 27, 2019): 1327. http://dx.doi.org/10.3390/w11071327.
Повний текст джерелаLe, Tuan Hoang, and Dung Anh To. "A Modified Semi-parametric Regression Model For Flood Forecasting." Science and Technology Development Journal 18, no. 2 (June 30, 2015): 95–105. http://dx.doi.org/10.32508/stdj.v18i2.1078.
Повний текст джерелаChen, Xinchi, Liping Zhang, Christopher James Gippel, Lijie Shan, Shaodan Chen, and Wei Yang. "Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2710457.
Повний текст джерелаAbdul Majid, M., M. Hafidz Omar, M. Salmi M. Noorani, and F. Abdul Razak. "River-flood forecasting methods: the context of the Kelantan River in Malaysia." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012021. http://dx.doi.org/10.1088/1755-1315/880/1/012021.
Повний текст джерелаPalash, Wahid, Yudan Jiang, Ali S. Akanda, David L. Small, Amin Nozari, and Shafiqul Islam. "A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity." Journal of Hydrometeorology 19, no. 1 (January 2018): 201–25. http://dx.doi.org/10.1175/jhm-d-16-0202.1.
Повний текст джерелаLe, Tuan Hoang, and Dung Anh To. "Short-term flood forecasting with an amended semi-parametric regression ensemble model." Science and Technology Development Journal 20, K2 (June 30, 2017): 117–25. http://dx.doi.org/10.32508/stdj.v20ik2.457.
Повний текст джерелаДисертації з теми "Flood forecasting Statistical methods"
何添賢 and Tim Yin Timothy Ho. "Forecasting with smoothing techniques for inventory control." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574286.
Повний текст джерелаNanzad, Bolorchimeg. "EVALUATION OF STATISTICAL METHODS FOR MODELING HISTORICAL RESOURCE PRODUCTION AND FORECASTING." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2192.
Повний текст джерелаLeung, Caleb Chee Shan. "Time series modelling of birth data." Thesis, Canberra, ACT : The Australian National University, 1995. http://hdl.handle.net/1885/118134.
Повний текст джерелаSilva, Jesús, Naveda Alexa Senior, Guliany Jesús García, Núẽz William Niebles, and Palma Hugo Hernández. "Forecasting Electric Load Demand through Advanced Statistical Techniques." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652142.
Повний текст джерелаKwok, Sai-man Simon, and 郭世民. "Statistical inference of some financial time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36885654.
Повний текст джерелаDavydenko, Andrey. "Integration of judgmental and statistical approaches for demand forecasting : models and methods." Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655734.
Повний текст джерелаVasiljeva, Polina. "Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190984.
Повний текст джерелаI denna uppsats analyserar vi det svårlösta problemet med att prognostisera utvecklingen för en valutakurs. Vi kombinerar maskininlärningsmetoder såsom agglomerativ hierarkisk klustring och Random Forest för att konstruera en modell i två steg med syfte att förutsäga utvecklingen av valutakursen mellan den svenska kronan och den amerikanska dollarn. Vi använder över 200 prediktorer bestående av olika finansiella och makroekonomiska tidsserier samt deras transformationer och utför prognoser för en vecka framåt med olika modellparametriseringar. En träffsäkerhet på i genomsnitt 53% erhålls, med några fall där en träffsäkerhet så hög som 60% kunde observeras. Det finns emellertid ingen tydlig indikation på att det existerar en kombination av de analyserade metoderna eller parametriseringarna som är optimal inom samtliga av de testade fallen. Vidare konstaterar vi att metoden är känslig för förändringar i underliggande träningsdata. Detta arbete har utförts på Tredje AP-fonden (AP3) och Kungliga Tekniska Högskolan (KTH).
Du, Toit Cornel. "Non-parametric volatility measurements and volatility forecasting models." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50401.
Повний текст джерелаENGLISH ABSTRACT: Volatilty was originally seen to be constant and deterministic, but it was later realised that return series are non-stationary. Owing to this non-stationarity nature of returns, there were no reliable ex-post volatility measurements. Subsequently, researchers focussed on ex-ante volatility models. It was only then realised that before good volatility models can be created, reliable ex-post volatility measuremetns need to be defined. In this study we examine non-parametric ex-post volatility measurements in order to obtain approximations of the variances of non-stationary return series. A detailed mathematical derivation and discussion of the already developed volatility measurements, in particular the realised volatility- and DST measurements, are given In theory, the higher the sample frequency of returns is, the more accurate the measurements are. These volatility measurements referred to above, however, all have short-comings in that the realised volatility fails if the sample frequency becomes to high owing to microstructure effects. On the other hand, the DST measurement cannot handle changing instantaneous volatility. In this study we introduce a new volatility measurement, termed microstructure realised volatility, that overcomes these shortcomings. This measurement, as with realised volatility, is based on quadratic variation theory, but the underlying return model is more realistic.
AFRIKAANSE OPSOMMING: Volatiliteit is oorspronklik as konstant en deterministies beskou, dit was eers later dat besef is dat opbrengste nie-stasionêr is. Betroubare volatiliteits metings was nie beskikbaar nie weens die nie-stasionêre aard van opbrengste. Daarom het navorsers gefokus op vooruitskattingvolatiliteits modelle. Dit was eers op hierdie stadium dat navorsers besef het dat die definieering van betroubare volatiliteit metings 'n voorvereiste is vir die skepping van goeie vooruitskattings modelle. Nie-parametriese volatiliteit metings word in hierdie studie ondersoek om sodoende benaderings van die variansies van die nie-stasionêre opbrengste reeks te beraam. 'n Gedetaileerde wiskundige afleiding en bespreking van bestaande volatiliteits metings, spesifiek gerealiseerde volatiliteit en DST- metings, word gegee. In teorie salopbrengste wat meer dikwels waargeneem word tot beter akkuraatheid lei. Bogenoemde volatilitieits metings het egter tekortkominge aangesien gerealiseerde volatiliteit faal wanneer dit te hoog raak, weens mikrostruktuur effekte. Aan die ander kant kan die DST meting nie veranderlike oombliklike volatilitiet hanteer nie. Ons stel in hierdie studie 'n nuwe volatilitieits meting bekend, naamlik mikro-struktuur gerealiseerde volatiliteit, wat nie hierdie tekortkominge het nie. Net soos met gerealiseerde volatiliteit sal hierdie meting gebaseer wees op kwadratiese variasie teorie, maar die onderliggende opbrengste model is meer realisties.
莫正華 and Ching-wah Mok. "A comparison of two approaches to time series forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977431.
Повний текст джерелаPharasi, Sid. "Development of statistical downscaling methods for the daily precipitation process at a local site." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99786.
Повний текст джерелаThe first component of this thesis consists of developing linear regression based downscaling models to predict both the occurrence and intensity of daily precipitation at a local site using stepwise, weighted least squares, and robust regression methods. The performance of these models was assessed using daily precipitation and NCEP re-analysis climate data available at Dorval Airport in Quebec for the 1961-1990 period. It was found that the proposed models could describe more accurately the statistical and physical properties of the local daily precipitation process as compared to the CGCM1 model. Further, the stepwise model outperforms the SDSM model for seven months of the year and produces markedly fewer outliers than the latter, particularly for the winter and spring months. These results highlight the necessity of downscaling precipitation for a local site because of the unreliability of the large-scale raw CGCM1 output, and demonstrate the comparative performance of the proposed stepwise model as compared with the SDSM model in reproducing both the statistical and physical properties of the observed daily rainfall series at Dorval.
In the second part of the thesis, a new downscaling methodology based on the principal component regression is developed to predict both the occurrence and amounts of the daily precipitation series at a local site. The principal component analysis created statistically and physically meaningful groupings of the NCEP predictor variables which explained 90% of the total variance. All models formulated outperformed the SDSM model in the description of the statistical properties of the precipitation series, as well as reproduced 4 out of 6 physical indices more accurately than the SDSM model, except for the summer season. Most importantly, this analysis yields a single, parismonious model; a non-redundant model, not stratified by month or season, with a single set of parameters that can predict both precipitation occurrence and intensity for any season of the year.
The third component of the research uses covariance structural modeling to ascertain the best predictors within the principal components that were developed previously. Best fit models with significant paths are generated for the winter and summer seasons via an iterative process. The direct and indirect effects of the variables left in the final models indicate that for either season, three main predictors exhibit direct effects on the daily precipitation amounts: the meridional velocity at the 850 HPa level, the vorticity at the 500 HPa level, and the specific humidity at the 500 HPa level. Each of these variables is heavily loaded onto the first three principal components respectively. Further, a key fact emerges: From season to season, the same seven significant large-scale NCEP predictors exhibit a similar model structure when the daily precipitation amounts at Dorval Airport were used as a dependent variable. This fact indicated that the covariance structural model was physically more consistent than the stepwise regression one since different model structures with different sets of significant variables could be identified when a stepwise procedure is employed.
Книги з теми "Flood forecasting Statistical methods"
Cunnane, C. Statistical distributions for flood frequency analysis. Geneva, Switzerland: Secretariat of the World Meteorological Organization, 1989.
Знайти повний текст джерелаKumar, Rakesh. Regional flood frequency analysis for sub-Himalayan region. Roorkee: National Institute of Hydrology, 1994.
Знайти повний текст джерелаParrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.
Знайти повний текст джерелаHolnbeck, Stephen R. Procedures for estimating unit hydrographs for large floods at ungaged sites in Montana. [Washington, D.C.]: U.S. G.P.O., 1996.
Знайти повний текст джерелаParrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.
Знайти повний текст джерелаParrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.
Знайти повний текст джерелаParrett, Charles. Methods for estimating flood frequency in Montana based on data through water year 1998. Helena, Mont: U.S. Dept. of the Interior, U.S. Geological Survey, 2004.
Знайти повний текст джерелаLang, Michel. Estimation de la crue centennale pour les plans de prévention des risques d'inondations. Versailles: Éditions Quæ, 2007.
Знайти повний текст джерелаPost, Claudia. Über die Anwendbarkeit geostatischer Verfahren und Optimierung von Daten zur Bewertung der hydraulischen und geologischen Gegebenheiten als Grundlage für Sanierungsmassnahmen am Beispiel des Ronneburger Erzreviers. Aachen: Lehrstuhl für Ingenieurgeologie und Hydrogeologie der RWTH, 2001.
Знайти повний текст джерелаStraub, T. D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.
Знайти повний текст джерелаЧастини книг з теми "Flood forecasting Statistical methods"
Hyndman, Rob J. "Business Forecasting Methods." In International Encyclopedia of Statistical Science, 185–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_156.
Повний текст джерелаŞen, Zekâi. "Probability and Statistical Methods." In Flood Modeling, Prediction and Mitigation, 245–301. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52356-9_6.
Повний текст джерелаBerlyand, M. E. "Statistical methods of air pollution forecasting." In Prediction and Regulation of Air Pollution, 159–201. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3768-3_6.
Повний текст джерелаCiepielowski, Andrzej. "Statistical Methods of Determining Typical Winter and Summer Hydrographs for Ungauged Watersheds." In Flood Hydrology, 117–24. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3957-8_10.
Повний текст джерелаWright, George, and Peter Ayton. "Psychological Aspects of Forecasting with Statistical Methods." In Operational Research and the Social Sciences, 617–23. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0789-1_93.
Повний текст джерелаMcClung, D. M. "Computer Assisted Avalanche Forecasting." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 347–58. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-3081-5_26.
Повний текст джерелаTao, Tao, and William C. Lennox. "Evaluation of Streamflow Forecasting Models." In Stochastic and Statistical Methods in Hydrology and Environmental Engineering, 77–85. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-3083-9_6.
Повний текст джерелаBianchi, Sergio, Fabrizio Di Sciorio, and Raffaele Mattera. "Forecasting VIX with Hurst Exponent." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 90–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99638-3_15.
Повний текст джерелаSerrano-Guerrero, Xavier, Luis-Fernando Siavichay, Jean-Michel Clairand, and Guillermo Escrivá-Escrivá. "Forecasting Building Electric Consumption Patterns Through Statistical Methods." In Advances in Intelligent Systems and Computing, 164–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32033-1_16.
Повний текст джерелаBřezková, Lucie, Milan Šálek, Petr Novák, Hana Kyznarová, and Martin Jonov. "New Methods of Flash Flood Forecasting in the Czech Republic." In IFIP Advances in Information and Communication Technology, 550–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22285-6_59.
Повний текст джерелаТези доповідей конференцій з теми "Flood forecasting Statistical methods"
Prohaska, Stevan, Aleksandra Ilić, and Pavla Pekarova. "ASSESSMENT OF STATISTICAL SIGNIFICANCE OF HISTORIC DANUBE FLOODS." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.05.
Повний текст джерелаPekárová, Pavla, Pavol Miklánek, Veronika Bačová Mitková, Marcel Garaj, and Ján Pekár. "ASSESSMENT HARMONIZATION PROBLEMS OF THE LONG RETURN PERIOD FLOODS ON THE DANUBE RIVER." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.16.
Повний текст джерелаJanál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.
Повний текст джерелаAl-Fawa'Reh, Mohammad, Alaa Hawamdeh, Rana Alrawashdeh, and Mousa Tayseer Jafar. "Intelligent Methods for flood forecasting in Wadi al Wala, Jordan." In 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE, 2021. http://dx.doi.org/10.1109/icoten52080.2021.9493425.
Повний текст джерелаAlsultanny, Yas. "Successful Forecasting for Knowledge Discovery by Statistical Methods." In 2012 Ninth International Conference on Information Technology: New Generations (ITNG). IEEE, 2012. http://dx.doi.org/10.1109/itng.2012.160.
Повний текст джерелаLei Liu, Yujian An, Zeyang Fan, and Wei Sun. "Quality evaluation based on multivariate statistical forecasting methods." In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). IEEE, 2016. http://dx.doi.org/10.1109/isie.2016.7745033.
Повний текст джерелаRussell, B. "Are Common Group Forecasting and Statistical Methods Valid?" In Canadian International Petroleum Conference. Petroleum Society of Canada, 2005. http://dx.doi.org/10.2118/2005-229.
Повний текст джерелаDominguez-Navarro, Jose Antonio, Iain Dinwoodie, and David McMillan. "Statistical forecasting for offshore wind helicopter operations." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960636.
Повний текст джерелаHristozov, Daniel, Nedyalko Katrandzhiev, Borislav Milenkov, and Eva Dimitrova. "Statistical methods for forecasting the expense of electrical energy." In 2019 Second Balkan Junior Conference on Lighting (Balkan Light Junior). IEEE, 2019. http://dx.doi.org/10.1109/blj.2019.8883541.
Повний текст джерелаNotaro, Vincenza, and Gabriele Freni. "Statistical analysis of the uncertainty related to flood hazard appraisal." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015 (ICCMSE 2015). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4938959.
Повний текст джерелаЗвіти організацій з теми "Flood forecasting Statistical methods"
Mendes, J., R. J. Bessa, H. Keko, J. Sumaili, V. Miranda, C. Ferreira, J. Gama, A. Botterud, Z. Zhou, and J. Wang. Development and testing of improved statistical wind power forecasting methods. Office of Scientific and Technical Information (OSTI), December 2011. http://dx.doi.org/10.2172/1031455.
Повний текст джерелаPerera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.
Повний текст джерелаWeissinger, Rebecca. Evaluation of hanging-garden endemic-plant monitoring at Southeast Utah Group national parks, 2013–2020. Edited by Alice Wondrak Biel. National Park Service, October 2022. http://dx.doi.org/10.36967/2294868.
Повний текст джерелаAnnual Mekong Flood Report 2017. Vientiane, Lao PDR: Mekong River Commission Secretariat, September 2019. http://dx.doi.org/10.52107/mrc.ajg54i.
Повний текст джерела