Dynamic data driven application system: Recent development and future perspective

Ecological Modelling

Volumn 204, Issue 1-2, 2007, Pages 1-8

  Ouyang, Ying ^a   Zhang, Jia En ^b   Luo, Shi Ming ^b  

^a ST JOHNS RIVER WATER MANAGEMENT DISTRICT   (United States)

^b SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Computational model; Dynamic data driven application system; Information technology; Measurement infrastructure

Indexed keywords

COMPUTER SIMULATION; FEEDBACK CONTROL; INFORMATION TECHNOLOGY; LARGE SCALE SYSTEMS; MATHEMATICAL MODELS; NATURAL SCIENCES COMPUTING;

COMPUTATIONAL MODELS; DYNAMIC DATA DRIVEN APPLICATION SYSTEM (DDDAS); FEEDBACK CONTROL SYSTEMS; SIMULATION MODELS;

DYNAMICAL SYSTEMS;

DATA ACQUISITION; DYNAMIC ANALYSIS; FUTURE PROSPECT; INFORMATION TECHNOLOGY; MEASUREMENT METHOD; NUMERICAL MODEL; SIMULATION;

EID: 34247219575     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2006.12.010     Document Type: Review

References (24)

1. Babovic V., and Abbott M.B. The evolution of equations from hydraulic data. Part II. Applications. J. Hydraul. Res. 35 3 (1997) 411-430
2. Bicknell B.R., Imhoff J.C., Kittle Jr. J.L., Jobes T.H., and Donigian Jr. A.S. Hydrological Simulation Program - Fortran, HSPF, Version 12, User's Manual. National Exposure Research Laboratory (2001), Office Of Research and Development, U.S. Environmental Protection Agency, Athens, GA March 2001
3. Center for Disease Control (CDC), 2005. About harmful algal blooms. http://www.cdc.gov/hab/factsheet.pdf.
4. Darema, F., 2005. Dynamic Data-driven Applications Systems: New Capabilities for Application Simulations and Measurements. Computational Science - ICCS 2005. Part 2. Lecture Notes in Computer Science, vol. 3515. Springer-Verlag, Berlin, Germany, pp. 610-615.
5. Danish Hydraulic Institute (DHI), 2005. MIKE SHE, Integrated surface and groundwater. http://www.dhisoftware.com/mikeshe/.
6. Douglas C.C., Shannon C.E., Efendiev Y., Ewing R., Ginting V., Lazarov R., Cole M.J., Jones G., Johnson C.R., and Simpson J. A note on data-driven contaminant simulation. Comput. Sci. - ICCS, Part 3: Proc., Lect. Notes Comput. Sci. 3038 (2004) 701-708
7. Douglas C.C., and Efendiev Y. A dynamic data-driven application simulation framework for contaminant transport problems. Comput. Math. Appl. 51 (2006) 1633-1646
8. Gaylor M., Seltzer M., Moulton S., and Freedman J. A dynamic, data-driven, decision support system for emergency medical services. Comput. Sci. - ICCS, Part 2: Lect. Notes Comput. Sci. 3515 (2005) 703-711
9. Haman, D.Z., Smajstra, A.G., Zazueta, F.S., Hensel, D.R., 1989. Water recycling in seepage irrigation of potatoes, Final report. St. Johns River Water Management District, Palatka, FL (Special Publication, SJ-89-SP 1).
10. Harris E.L., Babovic V., and Falconer R.A. Velocity prediction in compound channels with vegetated floodplains using genetic programming. Int. J. River Basin Mngt. 1 (2003) 117-123
11. Kim S., and Heller M. Emerging cyber infrastructure: challenges for the chemical process control community. Comput. Chem. Eng. 30 (2006) 1497-1501
12. Koza J. Genetic Programming: On the Programming of Computers by Natural Selection (1992), MIT Press, Cambridge, MA
13. Liong S.Y., Gautam T.R., Khu S.T., Babovic V., and Muttil N. Genetic Programming: a new paradigm in rainfall-runoff modelling. J. Am. Water Res. Assoc. 38 (2002) 577-584
14. Mandel J., Bennethum L.S., and Chen M.S. Towards a dynamic data driven application system for wildfire simulation. Lect. Notes Comput. Sci. 3515 (2005) 632-639
15. Matthews R., and Wassmannb R. Modeling the impacts of climate change and methane emission reductions on rice production: a review. Global Planet. Change 49 (2005) 203-221
16. Muttil M., and Lee J.H.W. Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecol. Model. 189 (2005) 363-376
17. National Science Foundation (NSF), 2000. Dynamic data driven application systems. 2000 NSF Workshop Report. http://www.nsf.gov/cise/cns/dddas/dd_das_work_shop_rprt.doc.
18. National Science Foundation (NSF), 2006. DDDAS: dynamic data driven applications systems, application examples. http://www.nsf.gov/cise/cns/dddas/DDDAS_Appendix.jsp.
19. Parashar M., Klie H., Ctalynrek U., Kurc T., Bangerth W., Matossian V., Saltz J., and Wheeler M.F. Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation Comput. Syst. 21 (2004) 19-26
20. Patrikalakis N.M., Mccarthy J.J., Robinson A.R., Schmidt H., Evangelinos C., Haley P.J., Lalis S., Lermusiaux P.F.J., Tian R., Leslie W.G., and Cho W. Towards a Dynamic Data Driven System for Rapid Adaptive Interdisciplinary Ocean Forecasting (2004), Massachusetts Institute of Technology, Cambridge, MA, USA. http://czms.mit.edu/poseidon/new1/publications/kluwer.pdf
21. US-EPA, 2004. Better Assessment Science Integrating Point and Nonpoint Sources, BASINS, Version 3.1. User's Manual. Office of Water, US-EPA, Washington, DC (EPA-823-C-04-004).
22. Whigham P.A., and Crapper P.F. Modelling rainfall-runoff relationships using genetic programming. Math. Compt. Model. 33 (2001) 707-721
23. Zazueta, F.S., Bucklin, R., Jones, P.H., Smajstrla, A.G., 2005. Basic concepts in environmental computer control of agricultural systems. http://edis.ifas.ufl.edu/AE003.
24. Zhao H., Knight D., Taskinoglu E., and Jovanovic V. Data driven design optimization methodology development and application. Comput. Sci. - ICCS, Part 3: Proc., Lect. Notes Comput. Sci. 3038 (2004) 748-755