图书馆订阅: Guest
国际不确定性的量化期刊

每年出版 6 

ISSN 打印: 2152-5080

ISSN 在线: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

EFFECT OF DEM UNCERTAINTY ON GEOPHYSICAL MASS FLOW VIA IDENTIFICATION OF STRONGLY COUPLED SUBSYSTEM

卷 9, 册 6, 2019, pp. 589-605
DOI: 10.1615/Int.J.UncertaintyQuantification.2019029044
Get accessGet access

摘要

With the recent advent of aerial photography, capturing high-resolution terrain information has provided new opportunities to simulate geophysical mass flow on high-resolution digital elevation models (DEMs). This gives a better understanding of the flow of debris that has a wide range of size. However, performing uncertainty quantification (UQ) of debris flow on an uncertain terrain profile, especially creating a hazard map, still poses a challenge. Even though there exist advanced statistical methods to model the DEM, UQ on the DEM requires the generation of a huge number of realizations that make the problem intractable. The current paper focuses on the usefulness of a recently developed UQ methodology that identifies Strongly Coupled Subsystems (SCS) in a large-scale uncertain dynamical system using suitable graph-clustering techniques. The method is used to create a parallel sampling scheme for a high-resolution DEM to enable faster UQ by integrating with traditional sampling methods, such as Monte Carlo or Latin hypercube sampling. The realizations are used to propagate the uncertainty in DEMs via a geophysical mass flow model simulated in TITAN2D. The accuracy of the UQ framework in estimating hazard maps is demonstrated by applying it to the block-and-ash flows resulting from the 1991 Colima Volcano, Mexico.

参考文献
  1. Volcano Discovery, Volcanoes of the World, accessed April 11,2019, from https://www.volcanodiscovery.com/volcanoes.html, 2019.

  2. U.S. Geological Survery, Volcano Hazard Programs, accessed April 11, 2019, from https://volcanoes.usgs.gov/index.html, 2019.

  3. Boudon, G., Camus, G., Gourgaud, A., and Lajoie, J., The 1984 Nuee-Ardente Deposits of Merapi Volcano, Central Java, Indonesia: Stratigraphy, Textural Characteristics, and Transport Mechanisms, Bull. Volcanol., 55(5):327-342, 1993.

  4. Petrie, G. andKennie, T., Terrain Modelling in Surveying and Civil Engineering, Comput. AidedDes., 19(4):171-187, 1987.

  5. Takahashi, T. and Tsujimoto, H., A Mechanical Model for Merapi-Type Pyroclastic Flow, J. Volcanol. Geotherm. Res., 98(1):91-115, 2000.

  6. Felix, G. and Thomas, N., Relation between Dry Granular Flow Regimes and Morphology of Deposits: Formation of Levees in Pyroclastic Deposits, Earth Planet. Sci. Lett., 221(1):197-213,2004.

  7. Luhr, J.F. and Carmichael, I.S., The Colima Volcanic Complex, Mexico: Part II. Late-Quaternary Cinder Cones, Contrib. Min. Pet, 76(2):127-147, 1981.

  8. Rueda, H., Erupciones Plinianas Del Holoceno En El Volcan Cerro Machin, Colombia, Estratigrafia, Petrografia Y Dinamica Eruptiva, PhD, MSc, Universidad Nacional Autonoma de Mexico, Mexico DF, 2005.

  9. Voight, B. and Calvache, M.L., Galeras Volcano, Colombia, in Encyclopedia of Natural Hazards, New York: Springer, pp. 369-377, 2013.

  10. U.S. Geologial Survery, The Climactic Eruption of May 18, 1980, accessed April 11, 2019, from https://pubs.usgs.gov/ gip/msh/climactic.html, 2019.

  11. Wikipedia, 2010 Eruptions of Mount Merapi, accessed April 11, 2019, from https://en.wikipedia.org/wiki/ 2010_eruptions_of_Mount_Merapi, 2019.

  12. Charbonnier, S.J., Germa, A., Connor, C.B., Gertisser, R., Preece, K., Komorowski, J.C., Lavigne, F., Dixon, T., and Connor, L., Evaluation of the Impact of the 2010 Pyroclastic Density Currents at Merapi Volcano from High-Resolution Satellite Imagery, Field Investigations and Numerical Simulations, J. Volcanol. Geotherm. Res., 261:295-315, 2013.

  13. Jousset, P., Pallister, J., Boichu, M., Buongiorno, M.F., Budisantoso, A., Costa, F., Andreastuti, S., Prata, F., Schneider, D., Clarisse, L., and Humaida, H., The 2010 Explosive Eruption of Java's Merapi Volcano-A 100-Year Event, J. Volcanol. Geotherm. Res, 241:121-135,2012.

  14. Thouret, J., Gupta, A., Lube, G., Liew, S.C., and Cronin, S., The 2006 Pyroclastic Deposits of Merapi Volcano, Java, Indonesia: High-Spatial Resolution IKONOS Images and Complementary Ground based Observations, Remote Sensing Environ, 114(9):1949-1967, 2010.

  15. Mitasova, H., Hofierka, J., Zlocha, M., andIverson, L.R., Modelling Topographic Potential for Erosion and Deposition Using GIS, Int. J. Geogr. Inf. Syst., 10(5):629-641, 1996.

  16. Patra, A.K., Bauer, A., Nichita, C., Pitman, E.B., Sheridan, M., Bursik, M., Rupp, B., Webber, A., Stinton, A., Namikawa, L., and Renschler, C.S., Parallel Adaptive Numerical Simulation of Dry Avalanches over Natural Terrain, J. Volcanol. Geotherm. Res, 139(1):1-21, 2005.

  17. Mukherjee, A., Rai, R., Singla, P., Singh, T., and Patra, A.K., Overlapping Clustering based Technique for Uncertainty Quan-tification in High-Dimensional Dynamical Systems, SIAM/ASA J. Uncertainty Quantif., 7:23-56, 2017.

  18. Jibson, R.W., Harp, E.L., and Michael, J.A., A Method for Producing Digital Probabilistic Seismic Landslide Hazard Maps, Eng. Geol., 58(3):271-289, 2000.

  19. Somerville, P.G., Smith, N.F., Graves, R.W., and Abrahamson, N.A., Modification of Empirical Strong Ground Motion At-tenuation Relations to Include the Amplitude and Duration Effects of Rupture Directivity, Seismol. Res. Lett., 68(1):199-222, 1997.

  20. Bonadonna, C., Connor, C.B., Houghton, B., Connor, L., Byrne, M., Laing, A., and Hincks, T., Probabilistic Modeling of Tephra Dispersal: Hazard Assessment of a Multiphase Rhyolitic Eruption at Tarawera, New Zealand, J. Geophys. Res., 110:B03203, 2005.

  21. Heuvelink, G.B., Error Propagation in Environmental Modelling with GIS, Boca Raton, FL: CRC Press, 1998.

  22. Oksanen, J. and Sarjakoski, T., Error Propagation of DEM-Based Surface Derivatives, Comput. Geosci., 31(8):1015-1027, 2005.

  23. Oksanen, J., Digital Elevation Model Error in Terrain Analysis, Helsingin, Finland: Helsingin University Press, 2006.

  24. Weng, Q., Quantifying Uncertainty of Digital Elevation Models Derived from Topographic Maps, in Advances in Spatial Data Handling, New York: Springer, pp. 403-418, 2002.

  25. Savage, S.B. and Hutter, K., The Motion of a Finite Mass of Granular Material Down a Rough Incline, J. Fluid Mechan., 199:177-215,1989.

  26. Sheridan, M., Stinton, A., Patra, A., Pitman, E., Bauer, A., and Nichita, C., Evaluating Titan2D Mass-Flow Model Using the 1963 Little Tahoma Peak Avalanches, Mount Rainier, Washington, J. Volcanol. Geotherm. Res., 139(1):89-102, 2005.

  27. Harten, A., Lax, P.D., and Leer, B.V., On Upstream Differencing and Godunov-Type Schemes for Hyperbolic Conservation Laws, SIAMRev., 25(1):35-61, 1983.

  28. Laszloffy, A., Long, J., and Patra, A.K., Simple Data Management, Scheduling and Solution Strategies for Managing the Irregularities in Parallel Adaptive HP Finite Element Simulations, Parallel Comput., 26(13):1765-1788, 2000.

  29. Patra, A., Laszloffy, A., and Long, J., Data Structures and Load Balancing for Parallel Adaptive HP Finite-Element Methods, Comput. Math. Appl., 46(1):105-123, 2003.

  30. GRASS Development Team, Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.2, Open Source Geospatial Foundation, 2017.

  31. Ehlschlaeger, C.R. and Goodchild, M.F., Uncertainty in Spatial Data: Defining, Visualizing, and Managing Data Errors, Proc. GIS/LIS, Phoenix, AZ, pp. 246-253, 1994.

  32. Stefanescu, E., Bursik, M., Cordoba, G., Dalbey, K., Jones, M., Patra, A., Pieri, D., Pitman, E., and Sheridan, M., Digital Elevation Model Uncertainty and Hazard Analysis Using a Geophysical Flow Model, Proc. R. Soc. A, 468:1543-1563,2012.

  33. Rasmussen, C.E. and Williams, C.K., Gaussian Process for Machine Learning, Cambridge: MIT Press, 2006.

  34. Mukherjee, A., Rai, R., Singla, P., Singh, T., and Patra, A.K., Comparison of Linearization and Graph Clustering Methods for Uncertainty Quantification of Large Scale Dynamical Systems, Int. J. Uncertainty Quantif., 7(1):23-56,2017.

  35. Roberts, J.B. and Spanos, P.D., Random Vibration and Statistical Linearization, Chichester, UK: Wiley, 2003.

  36. Banaszuk, A., Fonoberov, V.A., Frewen, T.A., Kobilarov, M., Mathew, G., Mezic, I., Pinto, A., Sahai, T., Sane, H., Speranzon, A., and Surana, A., Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems, Ann. Rev. Control, 35(1):77-98,2011.

  37. Xie, J., Kelley, S., and Szymanski, B.K., Overlapping Community Detection in Networks: The State-of-the-Art and Comparative Study, ACM Comput. Surv., 45(4):43, 2013.

  38. Blondel, V.D., Guillaume, J.L., Lambiotte, R., and Lefebvre, E., Fast Unfolding of Communities in Large Networks, J. Stat. Mech. Theory Exp, 2008(10):P10008, 2008.

  39. Ghanem, R.G. and Spanos, P.D., Stochastic Finite Elements: A Spectral Approach, Chichester, UK: Wiley, 2003.

  40. Atkinson, K. and Han, W., Numerical Solution of Fredholm Integral Equations of the Second Kind, Theor. Numer. Anal., 39:473-549, 2009.

  41. Hansen, P.C., Numerical Tools for Analysis and Solution of Fredholm Integral Equations of the First Kind, Inverse Probl., 8(6):849-872, 1992.

  42. Fishman, G.S., Monte Carlo: Concepts, Algorithms, and Applications, New York: Springer, 1996.

  43. Matsumoto, M. and Nishimura, T., Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator, ACM Trans. Model. Comput. Simul., 8(1):3-30, 1998.

  44. Marsaglia, G., The Structure of Linear Congruential Sequences, Applications of Number Theory to Numerical Analysis, New York: Elsevier, pp. 249-285, 1972.

  45. Marsaglia, G., Zaman, A., and Tsang, W.W., Toward a Universal Random Number Generator, Stat. Probab. Lett., 9(1):35-39, 1990.

  46. Stroud, A.H., Approximate Calculation of Multiple Integrals, Upper Saddle River, NJ: Prentice-Hall, 1971.

  47. Iman, R.L., Latin Hypercube Sampling, Hoboken, NJ: Wiley, 2008.

  48. Rupp, B., Bursik, M., Patra, A., Pitman, B., Bauer, A., Nichita, C., Saucedo, R., and Macias, J., Simulation of Pyroclastic Flows of Colima Volcano, Mexico, Using the TITAN2D Program, Geophys. Res. Abstracts, 5:12857, 2003.

  49. Pitman, E.B., Nichita, C.C., Patra, A., Bauer, A., Sheridan, M., and Bursik, M., Computing Granular Avalanches and Land-slides, Phys. Fluids, 15(12):3638-3646, 2003.

  50. Rupp, B., Bursik, M., Namikawa, L., Webb, A., Patra, A.K., Saucedo, R., Macias, J.L., and Renschler, C., Computational Modeling of the 1991 Block and Ash Flows at Colima Volcano, Mexico, Geol. Soc. Am. Special Papers, 402:237-252, 2006.

  51. Parker, J.A., Kenyon, R.V., and Troxel, D.E., Comparison of Interpolating Methods for Image Resampling, IEEE Trans. Med. Imaging, 2(1):31-39, 1983.

  52. SciPy, Grid Interpolation, accessed April 11, 2019, from https://docs.scipy.org/doc/scipy/reference/generated/ scipy.interpolate.griddata.html, 2019.

  53. Benson, A.R., Gleich, D.F., and Leskovec, J., Tensor Spectral Clustering for Partitioning Higher-Order Network Structures, Proc. SIAMInt. Conf. Data Min., 2015:118-126,2015.

  54. Shashua, A. and Hazan, T., Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision, Proc. of 22nd Int. Conf. Machine Learning, New York: ACM, pp. 792-799, 2005.

  55. Huang, F., Niranjan, U., Hakeem, M.U., and Anandkumar, A., Fast Detection of Overlapping Communities via Online Tensor Methods, Mach. Learn, arXiv:1309.0787, 2013.

对本文的引用
  1. Liu Xiangping, Ran Mengying, Xia Huimin, Deng Mingjun, Evaluating Vertical Accuracies of Open-Source Digital Elevation Models over Multiple Sites in China Using GPS Control Points, Remote Sensing, 14, 9, 2022. Crossref

Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集 订购及政策 Begell House 联系我们 Language English 中文 Русский Português German French Spain