Flood prediction using Time Series Data Mining

Journal of Hydrology

Volumn 333, Issue 2-4, 2007, Pages 305-316

  Damle, Chaitanya ^a   Yalcin, Ali ^a  

^a University of South Florida   (United States)

Author keywords

Chaotic systems; Event prediction; River flood forecasting; Time Series Data Mining

Indexed keywords

CHAOS THEORY; DATA MINING; MATHEMATICAL MODELS; RIVERS; TIME SERIES ANALYSIS;

CHAOTIC SYSTEMS; EVENT PREDICTION; RIVER FLOOD FORECASTING; TIME SERIES DATA MINING;

FLOODS;

CHAOS THEORY; DATA MINING; FLOODS; MATHEMATICAL MODELS; RIVERS; TIME SERIES ANALYSIS;

ARTIFICIAL NEURAL NETWORK; CHAOS THEORY; DATA MINING; FLOOD FORECASTING; NUMERICAL MODEL; PREDICTION; RISK ASSESSMENT; RIVER DISCHARGE; TIME SERIES ANALYSIS;

MISSISSIPPI RIVER; MISSOURI; NORTH AMERICA; SAINT LOUIS; UNITED STATES;

EID: 33845971941     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2006.09.001     Document Type: Article

References (29)

1. Abarbanel H.D.I. Analysis of Observed Chaotic Data (1996), Institute for Nonlinear Science
2. Ayewah N. Prediction of spatial temporal events using a hidden markov model (2003), Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX
3. van den Boogard H.F.P., Gautam D.K., and Mynett A.E. Auto-regressive neural networks for the modeling of time series. In: Babovic V., and Larsen C.L. (Eds). Hydroinformatics98 (1998), Balkema, Rotterdam 41-748
4. Buzug T., and Pfister G. Comparison of algorithms calculating optimal parameters for delay time coordinates. Physica D 58 (1992) 127-137
5. Buzug T., Reamers T., and Pfister G. Optimal reconstruction of strange attractors from purely geometrical arguments. Europhysics Letters 13 (1990) 605-610
6. Coulibaly P., Anctil F., and Bobée B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology 230 (2000) 244-257
7. Deo M.C., and Thirumalaiah K. Real time forecasting using neural networks. In: Govindaraju R.S., and Ramachandra Rao A. (Eds). Artificial Neural Networks in Hydrology (2000), Kluwer Academic Publishers, Dordrecht 3-71
8. Finnerty B.D., Smith M.B., Seo D.J., Koren V., and Moglen G.E. Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs. Journal of Hydrology 203 (1997) 21-38
9. Fraser A.M., and Swinney H.L. Independent coordinates for strange attractors from mutual information. Physics Review A 33 (1986) 1134-1140
10. Galka A. Topics in Nonlinear Time Series Analysis - With Implications for EEG Analysis (2000), World Scientific Publishing Company
11. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley, Reading, MA
12. Internet Website URL: http://www.usgs.gov/index.html, United States Geological Survey, 2004.
13. Islam M.N., and Sivakumar B. Characterization and prediction of runoff dynamics: a nonlinear dynamical view. Advances in Water Resources 25 (2002) 179-190
14. Kantz, H., Schrieber, T., 1997. Nonlinear time series analysis. Nonlinear Science Series, No. 7, Cambridge.
15. Knebl M.R., Yang Z.L., Hutchison K., and Maidment D.R. Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River basin. Journal of Environmental Management 75 (2005) 325-336
16. Laio F., Porporato A., Revelli R., and Ridolfi L. A comparison of nonlinear forecasting methods. Water Resources Research 39 5 (2003) 1129-1132
17. Palus M. Testing for nonlinearity using redundancies: quantitative and qualitative aspects. Physica D 80 (1995) 186-205
18. Porporato A., and Ridolfi L. Nonlinear analysis of river flow time sequences. Water Resources Research 33 6 (1997) 1353-1367
19. Porporato A., and Ridolfi L. Multivariate nonlinear prediction of river flows. Journal of Hydrology 248 (2001) 109-122
20. Povinelli, R.J., 1999. Time Series Data Mining: Identifying Temporal Patterns for Characterization and Prediction of Time Series Events. Ph.D. Dissertation, Marquette University, Milwaukee, WI.
21. Povinelli R.J., and Feng X. A new temporal pattern identification method for characterization and prediction of complex time series events. IEEE Transactions on Knowledge and Data Engineering 15 2 (2003) 339-352
22. Sivakumar B. Chaos theory in geophysics: past, present and future. Chaos, Solutions & Fractals 19 (2004) 441-462
23. Sivakumar B. Hydrologic modeling and forecasting: role of thresholds. Environmental Modeling and Software 20 5 (2005) 515-519
24. Sivakumar B., and Jayawardena A.W. An investigation of the presence of low-dimensional chaotic behaviour in the sediment transport phenomenon. Hydrological Sciences Journal 47 3 (2002) 405-416
25. Sivakumar B., and Wallender W.W. Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non-linear deterministic approach. Earth Surface Processes and Landforms 30 (2005) 665-677
26. Sivakumar B., Jayawardena A.W., and Fernando K.G. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology 265 (2002) 225-245
27. Takens F. Detecting strange attractors in fluid turbulence. In: Rand D., and Young L.-S. (Eds). Dynamical Systems and Turbulence (1981), Springer, Berlin 366-381
28. Theiler J., Eubank S., Longtin A., Galdrikian B., and Farmer J.D. Testing for non linearity in time series: the method of surrogate data. Physica D 58 (1992) 77-94
29. Xiong L., Shamseldin A.Y., and O'Connor K.M. A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system. Journal of Hydrology 245 (2001) 196-217