Application of fuzzy logic techniques in estimating the regional index flow for Michigan

Transactions of the ASABE

Volumn 56, Issue 1, 2013, Pages 103-115

  Hamaamin, Y A ^a   Nejadhashemi, A P ^a   Einheuser, M D ^b  

^a Michigan State University   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Fuzzy logic; Index flow; Low flow; Ungauged stream

Indexed keywords

10-FOLD CROSS-VALIDATION; ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANTHROPOGENIC ACTIVITY; FUZZY LOGIC TECHNIQUES; INDEX FLOW; LOW FLOW; MULTIPLE LINEAR REGRESSIONS; UNGAUGED STREAM;

CLIMATE CHANGE; LINEAR REGRESSION; STATISTICS; WATER RESOURCES; WATER SUPPLY; WATERSHEDS;

FUZZY LOGIC;

DISSOLVED OXYGEN; ESTIMATION METHOD; FUZZY MATHEMATICS; HUMAN ACTIVITY; INDEX METHOD; NUMERICAL MODEL; WATER FLOW; WATERSHED;

MICHIGAN; UNITED STATES;

EID: 84876825457     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (40)

1. Altunkaynak, A., M. Ozger, and M. Cakamakci. 2005. Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resources Mgmt. 19(5): 641-654.
2. Al-Zu'bi, Y., A. Sheta, and J. Al-Zu'bi. 2010. Nile River flow forecasting based on Takagi-Sugeno fuzzy model. J. Applied Sci. 10(4): 284-289.
3. Arthington, A. H., S. E. Bunn, N. L. Poff, and R. J. Naiman. 2006. The challenge of providing environmental flow rules to sustain river ecosystems. Ecol. Applications 16(4): 1311-1318.
4. Ayyub, B. M., and R. H. McCuen. 2003. Probability, Statistics, and Reliability for Engineers and Scientists. 2nd ed. Boca Raton, Fla.: CRC Press.
5. Bouckaert, R. R. 2003. Choosing between two learning algorithms based on calibrated tests. In Proc. 20th Intl. Conf. on Machine Learning (ICML-2003). Menlo Park, Cal.: AAAI Press.
6. Bubb, D. H., M. C. Lucas, T. J. Thom, and P. Rycroft. 2002. The potential use of PIT telemetry for identifying and tracking crayfish in their natural environment. Hydrobiologia 483(1-3): 225-230.
7. Chau, K. W., C. L. Wu, and Y. S. Li. 2005. Comparison of several flood forecasting models in Yangtze River. J. Hydrol. Eng. 10(6): 485-491.
8. Deka, P., and V. Chandramouli. 2005. Fuzzy neural network model for hydrologic flow routing. J. Hydrol. Eng. 10(4): 302-314.
9. Ghalhary, G. A. F., M. M. Baygi, and H. Nokhandan. 2009. Annual rainfall forecasting by using Mamdani fuzzy inference system. Res. J. Environ. Sci. 3(4): 400-413.
10. Gu, X., G. Song, and L. Xiao. 2006. Design of a fuzzy decisionmaking model and its application to software functional size measurement. In Proc. Intl. Conf. on Computational Intelligence for Modeling Control and Automation / Intl. Conf. on Intelligent Agents, Web Technologies, and Internet Commerce (CIMCA/IAWTIC '06). Piscataway, N.J.: IEEE.
11. Hamilton, D. A., R. C. Sorrell, and D. J. Holtschlag. 2008. A regression model for computing index flows describing the median flow for the summer month of lowest flow in Michigan. USGS Scientific Investigations Report 2008-5096.
12. Reston, Va.: U.S. Geological Survey. Huang, Y., Y. Lan, S. Thomson, A. Fang, W. C. Hoffmann, and R. E. Lacey. 2010. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agric. 71(2): 107-127.
13. Hundecha, Y., A. Bardossy, and H. W. Theisen. 2001. Development of a fuzzy logic-based rainfall-runoff model. Hydrol. Sci. 46(3): 363-376.
14. Jacquin, A. P., and A. Y. Shamseldin. 2006. Development of rainfall-runoff models using Takagi-Sugeno fuzzy inference systems. J. Hydrol. 329(1-2): 154-173.
15. Keskin, M. R., O. Terzi, and D. Taylan. 2004. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol. Sci. J. 49(6): 1001-1010.
16. Keskin, M. E., Ö. Terzi, and D. Taylan. 2009. Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor. Appl. Climatol. 98(1-2): 79-87.
17. Kisi, O. 2006. Daily pan evaporation modeling using a neurofuzzy computing technique. J. Hydrol. 329(3-4): 636-646.
18. Koster, B. S., S. E. Bunn, S. J. Mackay, N. L. Poff, R. J. Naiman, and P. S. Lake. 2010. The use of Bayesian networks to guide investments in flow and catchment restoration for impaired river ecosystems. Freshwater Biol. 55(1): 243-260.
19. Lee, K. S., and S. U. Kim. 2008. Identification of uncertainty in low flow frequency analysis using Bayesian MCMC method. Hydrol. Proc. 22(12): 1949-1964.
20. Library of Michigan. 2006. Michigan in brief: Information about the state of Michigan. Lansing, Mich.: Library of Michigan. Available at: www.michigan.gov/documents/hal-lm-MiB- 156795-7.pdf. Accessed 22 September 2011.
21. Mahabir, C., F. E. Hicks, and A. R. Fayek. 2003. Application of fuzzy logic to forecast seasonal runoff. Hydrol. Proc. 17(18): 3749-3762.
22. Mahabir, C., F. Hicks, and A. R. Fayek. 2006. Neuro-fuzzy river ice breakup forecasting system. Cold Regions Sci. and Tech. 46(2): 100-112.
23. Mahmood, Z., and S. Khan. 2009. On the use of k-fold crossvalidation to choose cutoff values and assess the performance of predictive models in stepwise regression. Intl. J. Biostatistics 5(1): doi: 10.2202/1557-4679.
24. Marza, V., and M. A. Seyyedi. 2009. Fuzzy multiple regression model for estimating software development time. Intl. J. Eng. Business Mgmt. 1(2): 79-82.
25. MCGI. 2005. Glacial deposits estimated transmissivity. Lansing, Mich.: Michigan Center for Geographic Information. Available at: www.mcgi.state.mi.us/ mgdl/?rel=ext&action=sext. Accessed 22 September 2011.
26. MCRP. 2004. Michigan climatological normals, 1971-2000.
27. East Lansing, Mich.: Michigan State University, State Climatologist's Office, Climatological Resources Program. MIRIS. 1978. MIRIS land use/cover polygon: Geographic data and metadata. Lansing, Mich.: Michigan Department of Natural Resources, Michigan Resource Information System. Available at: www.mcgi.state.mi.us/mgdl/. Accessed 22 September 2011.
28. Nayak, P. C. 2010. Explaining internal behavior in a fuzzy if-then rule-based flood-forecasting model. J. Hydrol. Eng. 15(1): 20-28.
29. Nayak, P. C., K. P. Sudheer, and K. S. Ramasastri. 2005. Fuzzy computing based rainfall-runoff model for real-time flood forecasting. Hydrol. Proc. 19(4): 955-968.
30. NORS. 1999. Overview of rivers in Michigan. Colorado Springs, Colo.: The National Organization for Rivers. Available at: www.nationalrivers.org/states/ mi-view.htm. Accessed 22 September 2011.
31. NRCS. 2009. Chapter 7: Hydrologic soil groups. In National Engineering Handbook, Part 630: Hydrology. Washington, D.C.: USDA Natural Resources Conservation Service. Available at: ftp://ftp.wcc.nrcs.usda.gov/wntsc/H&H/ NEH hydrology/ch7.pdf. Accessed 22 September 2011.
32. Pyrce, R. S. 2004. Hydrological low flow indices and their uses. WSC Report No. 04-2004.
33. Peterborough, Ontario, Canada: Watershed Science Centre. Sen, Z. 2010. Fuzzy Logic and Hydrological Modeling. Boca Raton, Fla.: CRC Press.
34. Shu, C., and T. B. M. J. Ouarda. 2008. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J. Hydrol. 349(1-2): 31-43.
35. Smakhtin, V. U. 2001. Low flow hydrology: A review. J. Hydrol. 240(1-2): 147-186.
36. State of Michigan. 2006. Public Acts of 2006: Act No. 33. Lansing, Michigan: State of Michigan. Available at: www.legislature.mi.gov/documents/ 2005-2006/publicact/pdf/ 2006-PA-0033.pdf. Accessed 22 September 2011.
37. Toprak, Z. F. 2009. Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT). Clean: Soil Air Water 37(9): 742-752.
38. Toprak, Z. F., E. Eris, N. Agiralioglu, H. K. Cigizoglu, L. Yilmaz, H. Aksoy, H. G. Coskun, G. Andic, and U. Alganci. 2009. Modeling monthly mean flow in a poorly gauged basin by fuzzy logic. Clean: Soil Air Water 37(7): 555-564.
39. Tzimopoulos, C., L. Mpallas, and C. Evangelides. 2008. Fuzzy model comparison to extrapolate rainfall data. J. Environ. Sci. and Tech. 1(4): 214-224.
40. WMO. 2008. Manual on low-flow estimation and prediction. Operational Hydrology Report No. 50 (WMO No. 1029). Geneva, Switzerland: World Metrological Organization.