Performance comparison of Adoptive Neuro Fuzzy Inference System (ANFIS) with Loading Simulation Program C++ (LSPC) model for streamflow simulation in El Niño Southern Oscillation (ENSO)-affected watershed

Expert Systems with Applications

Volumn 42, Issue 4, 2015, Pages 2213-2223

  Sharma, Suresh ^a   Srivastava, Puneet ^b   Fang, Xing ^b   Kalin, Latif ^c  

^a YOUNGSTOWN STATE UNIVERSITY   (United States)

^b AUBURN UNIVERSITY   (United States)

^c AUBURN UNIVERSITY   (United States)

Author keywords

Adaptive Neuro Fuzzy Inference System (ANFIS); El Ni o Southern Oscillation (ENSO); Hydrologic simulation; Loading Simulation Program C++ (LSPC)

Indexed keywords

ATMOSPHERIC PRESSURE; ATMOSPHERIC TEMPERATURE; C (PROGRAMMING LANGUAGE); CLIMATOLOGY; COMPUTER SIMULATION; FUZZY INFERENCE; FUZZY SYSTEMS; GAGES; NICKEL; OCEANOGRAPHY; RAIN; RAIN GAGES; SEA LEVEL; SURFACE WATERS; WATERSHEDS;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; HYDROLOGIC SIMULATIONS; HYDROLOGICAL SIMULATIONS; LOADING SIMULATION; NEURO-FUZZY INFERENCE SYSTEMS; PHYSICALLY BASED MODELING; SEA SURFACE TEMPERATURE (SST); SOUTHERN OSCILLATION;

LOADING;

EID: 84910653625     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.09.062     Document Type: Article

References (53)

1. Barsugli, J. J., Whitaker, J. S., Loughe, A. F., Sardeshmukh, P. D., & Toth, Z. (1999). The effect of the 1997/98 El Niño on individual large-scale weather events. Bulletin of the American Meteorological Society, 80, 1399-1411.
2. Bicknell, B., Imhoff, J., Kittle, J., Jr., Jobes, T., Donigian, A., Jr., & Johanson, R. (2001). Hydrological simulation program-fortran: HSPF, version 12 user's manual. Mountain View, California: AQUA TERRA Consultants.
3. Bosznay, M. (1989). Generalization of SCS curve number method. Journal of Irrigation and Drainage Engineering, 115, 139.
4. Chiew, F., Piechota, T., Dracup, J., & McMahon, T. (1998). El Niño/southern oscillation and Australian rainfall, streamflow and drought: Links and potential for forecasting. Journal of Hydrology, 204, 138-149.
5. Chiu, S. (1996). Method and software for extracting fuzzy classification rules by subtractive clustering. IEEE (pp. 461-465). .
6. Clarke, R. (1973). A review of some mathematical models used in hydrology, with observations on their calibration and use. Journal of Hydrology, 19, 1-20.
7. Cochocki, A., & Unbehauen, R. (1993). Neural networks for optimization and signal processing. John Wiley & Sons Inc..
8. Committee, A. T. (2000). Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering, 5, 115-123.
9. Crawford, N. H., & Linsley, R. K. (1966). Digital simulation in hydrology's stanford watershed model 4.
10. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.
11. Engel, B., Storm, D., White, M., Arnold, J., & Arabi, M. (2007). A hydrologic/water quality model applicati11. In Wiley online library.
12. Hansen, J., Jones, J., Irmak, A., & Royce, F. (2001). El Niño-southern oscillation impacts on crop production in the southeast United States. ASA Special Publication, 63, 55-76.
13. Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23, 665-685.
14. Jang, J. S. R., & Sun, C. T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83, 378-406.
15. Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. Automatic Control, IEEE Transactions on, 42, 1482-1484.
16. Jayawardena, A., Muttil, N., & Lee, J. (2006). Comparative analysis of data-driven and GIS-based conceptual rainfall-runoff model. Journal of Hydrologic Engineering, 11, 1.
17. Keener, V., Ingram, K., Jacobson, B., & Jones, J. (2007). Effects of El Niño/southern oscillation on simulated phosphorus loading in South Florida. Transactions of the ASAE, 50, 2081-2089.
18. Kessler, W. S. (2002). Is ENSO a cycle or a series of events? Geophysical Research Letters, 29, 40-41-40-44.
19. Khalil, A. F., McKee, M., Kemblowski, M., & Asefa, T. (2005). Basin scale water management and forecasting using artificial neural network. JAWRA Journal of the American Water Resources Association, 41, 195-208.
20. Kim, S. M., Benham, B. L., Brannan, K. M., Zeckoski, R. W., & Doherty, J. (2007). Comparison of hydrologic calibration of HSPF using automatic and manual methods. Water Resources Research, 43, W01402.
21. Kulkarni, J. (2000). Wavelet analysis of the association between the southern oscillation and the Indian summer monsoon. International Journal of Climatology, 20, 89-104.
22. Lim, K. J., Engel, B. A., Tang, Z., Choi, J., Kim, K. S., Muthukrishnan, S., & et al. (2005). Automated web Gis based hydrograph analysis tool, WHAT1. In Wiley online library.
23. Lukas, R., Hayes, S. P., & Wyrtki, K. (1984). Equatorial sea level response during the 1982-1983 El Niño. Journal of Geophysical Research: Oceans (1978-2012) (89), 10425-10430.
24. Makkeasorn, A., Chang, N.-B., & Zhou, X. (2008). Short-term streamflow forecasting with global climate change implications - A comparative study between genetic programming and neural network models. Journal of Hydrology, 352, 336-354.
25. McCabe, G. J., & Dettinger, M. D. (1999). Decadal variations in the strength of ENSO teleconnections with precipitation in the western United States. International Journal of Climatology, 19, 1399-1410.
26. Moriasi, D., Arnold, J., Van Liew, M., Bingner, R., Harmel, R., & Veith, T. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations 10.
27. Morid, S., Gosain, A., & Keshari, A. K. (2002). Comparison of the SWAT model and ANN for daily simulation of runoff in snowbound ungauged catchments. In Fifth international conference on hydroinformatics. Cardiff, UK.
28. Mukerji, A., Chatterjee, C., & Raghuwanshi, N. S. (2009). Flood forecasting using ANN, Neuro-Fuzzy, and Neuro-GA models. Journal of Hydrologic Engineering, 14, 647.
29. Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology, 10, 282-290.
30. Pascual, M., Rodó, X., Ellner, S. P., Colwell, R., & Bouma, M. J. (2000). Cholera dynamics and El Niño-southern oscillation. Science, 289, 1766.
31. Piechota, T. C., & Dracup, J. A. (1996). Drought and regional hydrologic variation in the United States: Associations with the el Niño-southern oscillation. Water Resources Research, 32, 1359-1373.
32. Pramanik, N., & Panda, R. K. (2009). Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54, 247-260.
33. Rajagopalan, B., & Lall, U. (1998). Interannual variability in western US precipitation. Journal of Hydrology, 210, 51-67.
34. Ropelewski, C., & Halpert, M. (1986). North American precipitation and temperature patterns associated with the El Niño/southern oscillation (ENSO). The Monthly Weather Review; (United States), 114.
35. Roy, S. S. (2006). The impacts of ENSO, PDO, and local SSTs on winter precipitation in India. Physical Geography, 27, 464-474.
36. Sharma, S. (2012). Incorporating El Niño southern oscillation (ENSO)-induced climate variability for long-range hydrologic forecasting and stream water quality protection. Auburn University.
37. Sharma, S., Srivastava, P., Fang, X., & Kalin, L. (in press). Hydrologic Simulation Approach for El Niño southern oscillation (ENSO)-affected watershed with limited rain gauge stations. Hydrologic Sciences Journal. http://www.tandfonline.com/doi/abs/10.1080/02626667.2014.952640#.VD-TYeK5eXg.
38. Sharma, S., Isik, S., Srivastava, P., & Kalin, L. (2012). Deriving spatially distributed precipitation data using the artificial neural network and multilinear regression models. Journal of Hydrologic Engineering, 18, 194-205.
39. Sharma, S., Srivastava, P., Fang, X., & Kalin, L. (2012). Incorporating climate variability for point-source discharge permitting in a complex river system. Transactions of the ASABE, 55, 2213-2228.
40. Sharma, S., Srivastava, P., Kalin, L., Fang, X., & Elias, E. (2014). Predicting total organic carbon load with El Nino southern oscillation phase using hybrid and fuzzy logic approaches. Transactions of the ASABE, 57(4), 1071-1085.
41. Shen, J., Parker, A., & Riverson, J. (2005). A new approach for a windows-based watershed modeling system based on a database-supporting architecture. Environmental Modelling & Software, 20, 1127-1138.
42. Taheri Shahraiyni, H., Ghafouri, M. R., Saghafian, B., & Bagheri Shouraki, S. (2013). Evaluation of a novel fuzzy method and a conceptual model for a long-term daily streamflow simulation. River Systems, 20, 249-260.
43. Talei, A., Chua, L. H. C., & Quek, C. (2010). A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling. Expert Systems with Applications, 37, 7456-7468.
44. Tayfur, G., Ozdemir, S., & Singh, V. P. (2003). Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Advances in Water Resources, 26, 1249-1256.
45. Tayfur, G., & Singh, V. P. (2006). ANN and fuzzy logic models for simulating eventbased rainfall-runoff. Journal of Hydraulic Engineering, 132, 1321-1330.
46. Tech, T., & Center, S. N. A. F. (2009). Loading simulation program in C++ (LSPC) Version 3.1 User's Manual. Fairfax, VA.
47. Thirumalaiah, K., & Deo, M. C. (2000). Hydrological forecasting using neural networks. Journal of Hydrologic Engineering, 5, 180-189.
48. Tokar, A. S., & Johnson, P. A. (1999). Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4, 232-239.
49. Trenberth, K. E., & Shea, D. J. (1987). On the evolution of the southern oscillation. Monthly Weather Review, 115, 3078-3096.
50. Trenberth, K. E., & Stepaniak, D. P. (2001). Indices of El Niño evolution. Journal of Climate, 14, 1697-1701.
51. Wang, P., Skahill, B., Samaitis, H., & Johnston, R. (2002). GIS-based artificial neural network and processed-based HSPF model for watershed runoff in Sinclair and dyes inlet, WA. Proceedings of the Water Environment Federation, 2002, 1547-1564.
52. Yan, H., Zou, Z., & Wang, H. (2010). Adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences, 22, 1891-1896.
53. Zadeh, L. A. (1965). Fuzzy sets?. Information and Control, 8, 338-353.