PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Development of Local IDF-formula Using Controlled Random Search Method for Global Optimization

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the study is to present the effective and relatively simple empirical approach to rainfall intensity-duration-frequency-formulas development, based on Controlled Random Search (CRS) for global optimization. The approach is mainly dedicated to the cases in which the commonly used IDF-relationships do not provide satisfactory fit between simulations and observations, and more complex formulas with higher number of parameters are advisable. Precipitation data from Gdańsk gauge station were analyzed as the example, with use of peak-overthreshold method and Chomicz scale for rainfall intensity. General forms of the IDF-function were chosen and the parameter calibration with use of CRS algorithm was developed. The compliance of the obtained IDFformulas with precipitation data and the efficiency of the algorithm were analyzed. The study confirmed the proposed empirical approach may be an interesting alternative for probabilistic ones, especially when IDFrelationship has more complex form and precipitation data do not match “typical” hydrological distributions.
Czasopismo
Rocznik
Strony
232--274
Opis fizyczny
Bibliogr. 80 poz., rys., tab.
Twórcy
  • Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Gdańsk, Poland
Bibliografia
  • 1. Akan, A.O., and R.J. Houghtalen (2003), Urban hydrology, Hydraulics, and Stormwater Quality: Engineering Applications and Computer Modeling, John Wiley & Sons, Inc., Hoboken.
  • 2. Ali, M.M. (1994), Some modified stochastic global optimization algorithms with applications, Ph.D. Thesis, Loughborough University of Technology, Loughborough, England,https://dspace.lboro.ac.uk/2134/13429.
  • 3. Ali, M.M., and C. Storey (1994), Topographical multilevel single linkage, J. Global Optim. 5, 4, 349-458, DOI: 10.1007/BF01096684.
  • 4. Ali, M.M., and C. Storey (1997), Aspiration based simulated annealing algorithm, J. Global Optim. 11, 2, 181-191, DOI: 10.1023/A:1008202703889.
  • 5. Ali, M.M., C. Storey, and A. Törn (1997a), Application of stochastic global optimization algorithms to practical problems, J. Optimiz. Theory Appl. 95, 3, 545-563, DOI: 10.1023/A:1022617804737.
  • 6. Ali, M.M., A. Törn, and S. Viitanen (1997b), A numerical comparison of some modified controlled random search algorithms, J. Global Optim. 11, 4, 377-385, DOI: 10.1023/A:1008236920512.
  • 7. Ariff, N.M., A.A. Jemain, K. Ibrahim, and W.Z. Wan Zin (2012), IDF relationships using bivariate copula for storm events in Peninsular Malaysia, J. Hydrol. 470-471, 158-171, DOI: 10.1016/j.jhydrol.2012.08.045.
  • 8. Arnell, V. (1982), Rain fall data for the design of sewer pipe systems, Report Series A:8, Chalmers University of Technology, Department of Hydraulics, Göteborg, Sweden.
  • 9. Ben-Zvi, A. (2009), Rainfall intensity-duration-frequency relationship derived from large partial duration series, J. Hydrol. 367, 1-2, 104-114, DOI: 10.1016/j.jhydrol.2009.01.007.
  • 10. Bonnans, J.F., J.C. Gilbert, C. Lemaréchal, and C.A. Sagastizábal (2006), Numerical Optimization: Theoretical and Practical Aspects, Springer-Verlag, Berlin Heidelberg.
  • 11. Chow, V.T. (1964), Handbook of Applied Hydrology: A Compendium of Water-resources Technology, McGraw-Hill Book Co., New York.
  • 12. Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, Springer, London, 208 pp.
  • 13. Cunnane, C. (1973), A particular comparison of annual maxima and partial duration series methods of flood frequency prediction, J. Hydrol. 18, 3-4, 257-271, DOI: 10.1016/0022-1694(73)90051-6.
  • 14. De Michele, C., and G. Salvadori (2005), Some hydrological applications of small sample estimators of Generalized Pareto and Extreme Value distributions, J. Hydrol. 301, 1-4, 37-53, DOI: 10.1016/j.jhydrol.2004.06.015.
  • 15. Dekkers, A., and E. Aarts (1991), Global optimization and simulated annealing, Math. Program. 50, 1-3, 367-393, DOI: 10.1007/BF01594945.
  • 16. Delleur, J.W. (2003), The evolution of urban hydrology: Past, present, and future, J. Hydraul. Eng. ASCE 129, 8, 563-573, DOI: 10.1061/(ASCE)0733-9429(2003)129:8(563).
  • 17. Dysarz, T., and J.J. Napiórkowski (2002), Flood control in Nysa Reservoir System by means of sequential optimisation and CRS method. In: Proc. Workshop on Evolutionary Algorithms and Global Optimization, 23-25 September 2002, Cracow, Poland, Warsaw Technical University Publ., Warszawa, 27-33.
  • 18. Dysarz, T., and J.J. Napiórkowski (2003), Optimal flood control of Nysa Kłodzka Reservoir System, Publs. Inst. Geophys. Pol. Acad. Sc. E-3, 365, (J.J. Napiórkowski (ed.), Modelling and Control of Floods), 83-96.
  • 19. El-Sayed, E.A.H. (2011), Generation of rainfall intensity duration frequency curves for ungauged sites, Nile Basin Water Sci. Eng. J. 4, 1, 112-124.
  • 20. Elsebaie, I.H. (2012), Developing rainfall intensity-duration-frequency relationship for two regions of Saudi Arabia, J. King Saud Univ. - Eng. Sci. 24, 2, 131-140, DOI: 10.1016/j.jksues.2011.06.001.
  • 21. Endreny, T.A., and N. Imbeah (2009), Generating robust rainfall intensity-duration-frequency estimates with short-record satellite data, J. Hydrol. 371, 1-4, 182-191, DOI: 10.1016/j.jhydrol.2009.03.027.
  • 22. Fletcher, R. (1965), Function minimization without evaluating derivatives - a review, Comput. J. 8, 1, 33-41, DOI: 10.1093/comjnl/8.1.33.
  • 23. Fylstra, D., L. Lasdon, J. Watson, and A. Waren (1998), Design and use of the Microsoft Excel Solver, Interfaces 28, 5, 29-55, DOI: 10.1287/inte.28.5.29.
  • 24. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison–Wesley Publ. Co., Reading, 412 pp.
  • 25. Grace, R.A., and P.S. Eagleson (1967), A model for generating synthetic sequences of short-time-interval rainfall depths. In: Proc. Int. Hydrology Symposium, 6-8 September 1967, Fort Collins, Colorado, USA, Vol. 1, Colorado State University, Colorado, 268-276.
  • 26. Grimaldi, S., and F. Serinaldi (2006), Design hyetograph analysis with 3-copula function, Hydrol. Sci. J. 51, 2, 223-238, DOI: 10.1623/hysj.51.2.223.
  • 27. Hailegeorgis, T.T., S.T. Thorolfsson, and K. Alfredsen (2013), Regional frequency analysis of extreme precipitation with consideration of uncertainties to up-date IDF curves for the city of Trondheim, J. Hydrol. 498, 305-318, DOI: 10.1016/j.jhydrol.2013.06.019.
  • 28. Johansen, L. (1979), Design rainfalls for sewer systems, Rep. 79-2, Dept. of Sanitary Eng., Technical University of Denmark, Copenhagen, Denmark.
  • 29. Katz, R.W., M.P. Parlange, and P. Naveau (2002), Statistics of extremes in hydrology, Adv. Water Resour. 25, 8-12, 1287-1304, DOI: 10.1016/S0309-1708(02)00056-8.
  • 30. Kisiel, C.C., L. Duckstein, and M.M. Fogel (1971), Analysis of ephemeral flow in aridlands, J. Hydraul. Div. ASCE97, 10, 1699-1717.
  • 31. Koutsoyiannis, D. (2004a), Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation, Hydrol. Sci. J. 49, 4, 575-590, DOI: 10.1623/hysj.49.4.575.54430.
  • 32. Koutsoyiannis, D. (2004b), Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records, Hydrol. Sci. J. 49, 4, 591-610, DOI: 10.1623/hysj.49.4.591.54424.
  • 33. Koutsoyiannis, D., D. Kozonis, and A. Manetas (1998), A mathematical framework for studying rainfall intensity-duration-frequency relationships, J. Hydrol. 206, 1-2, 118-135, DOI: 10.1016/S0022-1694(98)00097-3.
  • 34. Langousis, A., and D. Veneziano (2007), Intensity-duration-frequency curves from scaling representations of rainfall, Water Resour. Res. 43, 2, W02422, DOI: 10.1029/2006WR005245.
  • 35. Licznar, P., and J. Łomotowski (2005), Analysis of instantaneous intensities of design rainfalls in Wroclaw, Ochr. Sr. 27, 2, 25-28 (in Polish).
  • 36. Licznar, P., and J. Łomotowski (2007), Rainfall Kinetic Energy Measurements with Impactometer Implementation, Works and Studies, Vol. 69, Institute of Environmental Engineering, Polish Academy of Sciences, Zabrze, 70 pp.
  • 37. Licznar, P., T.G. Schmitt, and D.E. Rupp (2011a), Distributions of microanonical cascade weights of rainfall at small timescales, Acta Geophys. 59, 5, 1013-1043, DOI: 10.2478/s11600-011-0014-4.
  • 38. Licznar, P., J. Łomotowski, and D.E. Rupp (2011b), Random cascade driver rainfall disaggregation for urban hydrology: An evaluation of six models and a new generator, Atmos. Res. 99, 3-4, 563-578, DOI: 10.1016/j.atmosres.2010.12.014.
  • 39. Llasat, M.-C. (2001), An objective classification of rainfall events on the basis of their convective features: application to rainfall intensity in the northeast of Spain, Int. J. Climatol. 21, 11, 1385-1400, DOI: 10.1002/joc.692.
  • 40. Lutgens, F.K., and E.J. Tarbuck (2004), The Atmosphere. An Introduction to Meteorology, 9th ed., Pearson Education Inc., New Jersey.
  • 41. Madsen, H., P.F. Rasmussen, and D. Rosbjerg (1997a), Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events: 1. At-site modeling, Water Resour. Res. 33, 4, 747-757, DOI: 10.1029/96WR03848.
  • 42. Madsen, H., C.P. Pearson, and D. Rosbjerg (1997b), Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events: 2. Regional modeling, Water Resour. Res. 33, 4, 759-769, DOI: 10.1029/96WR03849.
  • 43. Manzanares-Filho, N., and R.B.F. Albuquerque (2008), Accelerating controlled random search algorithms using a distribution strategy. In: Proc. Int. Conf. Engineering Optimization (EngOpt 2008), 1-5 June 2008, Rio de Janeiro, Brazil.
  • 44. Manzanares-Filho, N., C.A.A. Moino, and A.B. Jorge (2005), An improved controlled random search algorithm for inverse airfoil cascade design. In: Proc. 6th World Congress of Structural and Multidisciplinary Optimization (WCSMO-6), 30 May – 3 June 2005, Rio de Janeiro, Brazil, paper No. 4451.
  • 45. Maraun, D., T.J. Osborn, and N.P. Gillett (2008), United Kingdom daily precipitation intensity: improved early data, error estimates and an update from 2000 to 2006, Int. J. Climatol. 28, 6, 833-842, DOI: 10.1002/joc.1672.
  • 46. Marsalek, J. (1978), Research on the design storm concept, ASCE Urban Water Resources Research Program, Technical Memorandum No. 33, New York.
  • 47. McCuen, R.H. (2005), Hydrologic Analysis and Design, 3rd ed., Pearson Prentice Hall, Englewood Cliffs, 859 pp.
  • 48. Molnar, P., and P. Burlando (2005), Preservation of rainfall properties in stochastic disaggregation by a simple random cascade model, Atmos. Res. 77, 1-4, 137-151, DOI: 10.1016/j.atmosres.2004.10.024.
  • 49. Mutzner, H. (1991), The significance of areal rainfall distribution for flows from a very small urban drainage catchment, Atmos. Res. 27, 1-3, 99-107, DOI: 10.1016/0169-8095(91)90011-K.
  • 50. Nagy, J.T. (1991), Evaluation of rainfall characteristics in Bratislava, Atmos. Res. 27, 1-3, 209-217, DOI: 10.1016/0169-8095(91)90020-W.
  • 51. Pierre, D.A. (1969), Optimization Theory with Applications, John Wiley &Sons Inc., New York.
  • 52. Powell, M.J.D. (1964), An efficient method for finding the minimum of a function of several variables without calculating derivatives, Comput. J. 7, 2, 155-162, DOI:10.1093/comjnl/7.2.155.
  • 53. Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery (2007), Numerical Recipes: the Art of Scientific Computing, 3rd ed., Cambridge University Press, New York.
  • 54. Price, W.L. (1977), A controlled random search procedure for global optimisation, Comput. J. 20, 4, 367-370, DOI: 10.1093/comjnl/20.4.367.
  • 55. Price, W.L. (1978), Controlled random search procedure for global optimization. In: L.C.W. Dixon, and G.P. Szegö (eds.), Toward Global Optimization 2, North-Holland Publ. Co., Amsterdam, 71-84.
  • 56. Price, W.L. (1983), Global optimization by controlled random search, J. Optimiz. Theor. Appl. 40, 3, 333-348, DOI: 10.1007/BF00933504.
  • 57. Pui, A., A. Sharma, R. Mehrotra, B. Sivakumar, and E. Jeremiah (2012), A comparison of alternatives for daily and sub-daily rainfall disaggregation, J. Hydrol. 470-471, 138-157, DOI: 10.1016/j.jhydrol.2012.08.041.
  • 58. Reiss, R.-D., and M. Thomas (2007), Statistical Analysis of Extreme Values with Applications to Insurance, Finance, Hydrology and Other Fields, 3rd ed., Birkhäuser Verlag, Basel.
  • 59. Rinaudo, S., F. Moschella, O. Muscato, and M.A. Ahile (1998), Controlled random search parallel algorithm for global optimization with distributed processes on multivendor CPUs. In: L. Arkeryd, J. Bergh, Ph. Brenner, and R. Petter-son (eds.), Proc. 10th Conf. European Consortium for Mathematics in Industry “Progress in Industrial Mathematics at ECMI 98”, 23-28 June 1998, Gothenburg, Sweden, 271-278.
  • 60. Rinnooy Kan, A.H.G., and G.T. Timmer (1987), Stochastic global optimization methods. Part II: Multi level methods, Math. Program. 39, 1, 57-78, DOI: 10.1007/BF02592071.
  • 61. Romanowicz, R.J., M. Osuch, and M. Grabowiecka (2013), On the choice of calibration periods and objective functions: A practical guide to model parameter identification, Acta Geophys. 61, 6, 1477-1503, DOI: 10.2478/s11600-013-0157-6.
  • 62. Schilling, W. (1991), Rainfall data for urban hydrology: what do we need?, Atmos. Res. 27, 1-3, 5-21, DOI: 10.1016/0169-8095(91)90003-F.
  • 63. Sevruk, B., and H. Geiger (1987), Frequency distributions preferred by hydrologists. In: B.C. Yen (ed.), Proc. 4th Int. Conf. in Urban Storm Drainage “Topics in Urban Drainage Hydraulics and Hydrology”, Session D, 31 August -4 September 1987, Lausanne, Switzerland, 51-52.
  • 64. Suligowski, R. (2004), Temporal and spatial structure of the rainfall in Poland. Attempt at the regionalization, Publ. of Jan Kochanowski University, Institute of Geography, Kielce (in Polish).
  • 65. Svensson, C., R.T. Clarke, and D.A. Jones (2007), An experimental comparison of methods for estimating rainfall intensity-duration-frequency relations from fragmentary records, J. Hydrol. 341, 1-2, 79-89, DOI: 10.1016/j.jhydrol. 2007.05.002.
  • 66. Tanaka, S., and K. Takara (2002), A study of threshold selection in POT analysis of extreme floods. In: A. Snorasson, H.P. Finnsdóttir, and M. Moss (eds.), Proc. Symp. “The Extremes of the Extremes: Extraordinary Floods”, Reykjavik, Iceland, IAHS Publications, No. 271, IAHS Press, Wallingford, 299-306.
  • 67. Tavares, L.V., and J.E. Da Silva (1983), Partial duration series method revisited, J. Hydrol. 64, 1-4, 1-14, DOI: 10.1016/0022-1694(83)90056-2.
  • 68. Todorovic, P. (1978), Stochastic models of floods, Water Resour. Res. 14, 2, 345-356, DOI: 10.1029/WR014i002p00345.
  • 69. Trefry, C.M., D.W. Watkins, and D. Johnson (2005), Regional rainfall frequency analysis for the State of Michigan, J. Hydrol. Eng. ASCE 10, 6, 437-449, DOI: 10.1061/(ASCE)1084-0699(2005)10:6(437).
  • 70. Tsoulos, I.G., and I.E. Lagaris (2006), Genetically controlled random search: a global optimization method for continuous multidimensional functions, Comp. Phys. Comm. 174, 2, 152-159, DOI: 10.1016/j.cpc.2005.09.007.
  • 71. Vaes, G., P. Willems, and J. Berlamont (2001), Rainfall input requirements for hydrological calculations, Urban Water 3, 1-2, 107-112, DOI: 10.1016/S1462-0758(01)00020-6.
  • 72. Venkata Ramana, R., B. Chakravorty, N.R. Samal, N.G. Pandey, and P. Mani (2008), Development of intensity duration frequency curves using L-moment and GIS technique, J. Appl. Hydrol. 21, 1-2, 88-100.
  • 73. Vukmirović, V., and J. Petrović (1991), Statistical analysis of storms - a basis for urban runoff modeling. In: Č. Maksimović (ed.), Proc. of Int. Conf. “New Technologies in Urban Drainage UDT’91”, 17-21 June 1991, Dubrovnik, Yugoslavia, Elsevier, London, 13-19.
  • 74. Walesh, S.G. (1989), Urban Surface Water Management, John Wiley & Sons, Inc., New York.
  • 75. Wenzel, H.G. Jr., and M.L. Voorhees (1978), Evaluation of design storm concept (presented at the AGU Fall Meeting, December 1978, San Francisco, California), EOS Trans. AGU 59, 12, 1070.
  • 76. Willems, P. (2000), Compound intensity/duration/frequency-relationships of extreme precipitation for two seasons and two storm types, J. Hydrol. 233, 1-4, 189-205, DOI: 10.1016/S0022-1694(00)00233-X.
  • 77. Wołoszyn, E. (1991), Polish rainfall-runoff investigations and modification of the rational method, Atmos. Res. 27, 1-3, 219-229, DOI: 10.1016/0169-8095(91)90021-N.
  • 78. Wołoszyn, E. (2003), The catastrophic flood in Gdańsk on July 2001. In: R. Arsov, J. Marsalek, E. Watt, and E. Zeman (eds.), Urban Water Management: Science Technology and Service Delivery, NATO Science Series, Vol. 25, Springer, Dordrecht, 115-124, DOI: 10.1007/978-94-010-0057-4_12.
  • 79. Wołoszyn, E. (2009), Analysis of rainfall data of Gdansk Meteorological Station. In: C. Popowska and M. Jovanovski (eds.), Proc. 11th Int. Symp. on Water Management and Hydraulic Engineering, 1-5 September 2009, Univ. Ss. Cyril and Methodius, Faculty of Civil Engineering, Orhid, Macedonia, 675-684.
  • 80. Yevjevich, V. (1972), Probability and Statistics in Hydrology, Water Resources Publ., Fort Collins, USA.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1ba43ca5-77b0-4e18-a8c6-9f0b137f131f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.