River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques

Computers and Geosciences

Volumn 43, Issue , 2012, Pages 73-82

  Kisi, Ozgur ^a   Shiri, Jalal ^b  

^a CANIK BASARI UNIVERSITY   (Turkey)

^b UNIVERSITY OF TABRIZ   (Iran)

Author keywords

Genetic programming; Hydro climatology; Sediment; Soft computing

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; CALIFORNIA , USA; CLIMATIC VARIABLES; COMPARATIVE STUDIES; COMPARISON RESULT; CONJUGATE GRADIENT; CONJUGATE GRADIENT ALGORITHMS; DAILY RAINFALL; DATA SETS; GENE EXPRESSION PROGRAMMING; GRADIENT DESCENT TRAINING; HYDRO-CLIMATOLOGY; LEVENBERG-MARQUARDT; RIVER SUSPENDED SEDIMENTS; SOFT COMPUTING METHODS; SOFTCOMPUTING TECHNIQUES; SUSPENDED SEDIMENT CONCENTRATIONS;

ALGORITHMS; CLIMATOLOGY; CONJUGATE GRADIENT METHOD; ESTIMATION; GENETIC PROGRAMMING; NEURAL NETWORKS; SEDIMENTATION; SEDIMENTOLOGY; SEDIMENTS; SOFT COMPUTING; SUSPENDED SEDIMENTS; WATER RESOURCES;

RIVERS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLIMATE EFFECT; ESTIMATION METHOD; FLUVIAL DEPOSIT; FUZZY MATHEMATICS; MATHEMATICAL ANALYSIS; NUMERICAL MODEL; RAINFALL; SEDIMENT TRANSPORT; STREAMFLOW; SUSPENDED SEDIMENT;

CALIFORNIA; EEL RIVER; UNITED STATES;

EID: 84859640789     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2012.02.007     Document Type: Article

References (36)

1. ASCE Task Committee Artificial neural networks in hydrology. I: preliminary concepts. ASCE Journal of Hydrologic Engineering 2000, 5(2):115-123.
2. ASCE Task Committee Artificial neural networks in hydrology. I: Hydrologic applications. ASCE Journal of Hydrologic Engineering 2000, 5(2):124-137.
3. Aytek A., Kisi O. A genetic programming approach to suspended sediment modeling. Journal of Hydrology 2008, 351:288-298.
4. Bae D.H., Jeong D.M., Kim G. Monthly dam inflow forecasts using weather forecast information and neuro-fuzzy technique. Hydrological Sciences Journal 2007, 52(1):99-113.
5. Banzhaf W., Nordin P., Keller P.E., Francone F.D. Genetic Programming 1998, Morgan Kaufmann, San Francisco, CA, 512 pp.
6. Bazartseren B., Hildebrandt G., Holz K.P. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 2003, 55(3-4):439-450.
7. Cigizoglu H.K. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in water resources 2004, 27:185-195.
8. Cigizoglu H.K., Kisi O. Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology 2006, 317:221-238.
9. Cobaner M., Unal B., Kisi O. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of Hydrology 2009, 367(1-2):52-61.
10. Dawson W.C., Wilby R. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal 1998, 43(1):47-66.
11. Ferreira, C., 2001a. Gene expression programming in problem solving. In: 6th Online World Conference on Soft computing in Industrial Applications (invited tutorial).
12. Ferreira C. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems 2001, 13(2):87-129.
13. Ferreira C. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence 2006, Springer, Berling, Heidelberg New York, NY, USA, 478 pp.
14. Giustolisi O. Using GP to determine Chezzy resistance coefficient in corrugated channels. Journal of Hydroinformatics 2004, 6(3):157-173.
15. Haykin S. Neural Networks - A Comprehensive Foundation 1998, Prentice-Hall, Upper Saddle River, NJ, 26-32. 2nd. edn.
16. Jain S.K. Development of integrated sediment rating curves using ANNs. ASCE Journal of Hydrologic Engineering 2001, 127(1):30-37.
17. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 1993, 23(3):665-685.
18. Jang J.S.R., Sun C.T., Mizutani E. Neurofuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence 1997, Prentice-Hall, New Jersey.
19. Johari A., Habibagahi G., Ghahramani A. Prediction of soil-water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering 2006, 132(5):661-665.
20. Keskin M.E., Terzi O., Taylan D. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrological Sciences Journal 2004, 49(6):1001-1010.
21. Kisi O. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 2005, 50(4):683-696.
22. Kisi, O., Shiri, J., 2010. A comparison of genetic programming and ANFIS in forecasting daily, monthly and daily stremflows. In: Proceedings of the International symposium on innovations in intelligent systems and applications. pp. 118-122, 21-24 June 2010, Kayseri and Cappadocia, Turkey.
23. Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man Machine Studies 1975, 7(1):1-13.
24. Ploi, R., McPhee, N.F., 2008. Covariant parsimony pressure for genetic programming. Technical report CES-480, ISSN:1744-8050.
25. Rai R.K., Mathur B.S. Event-based sediment modeling using artificial neural network. Water Resources Management 2008, 22(4):423-441.
26. Savic A.D., Walters A.G., Davidson J.W. A genetic programming approach to rainfall-runoff modeling. Water Resources Management 1999, 13:219-231.
27. Shiri J., Kisi O. Short term and long term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology 2010, 394:486-493.
28. Shiri J., Kisi O. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers & Geosciences 2011, 37(10):1692-1701.
29. Shiri J., Dierickx W., Pour-Ali Baba A., Nemati S., Ghorbani M.A. Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system and artificial neural network. Hydrology Research 2011, 42(6):491-502.
30. Shiri J., Makarynskyy O., Kisi O., Dierickx W., Fakheri Fard A. Prediction of short term operational water levels using an adaptive neuro-fuzzy inference system. ASCE Journal of Waterway, Port, Coastal, and Ocean Engineering 2011, 10.1061/(ASCE)WW.1943-5460.0000097.
31. Shiri J., Kisi O., Landeras G., Lopez J.J., Nazemi A.H., Stuyt L.C.P.M. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northwestern Spain). Journal of Hydrology 2012, 414-415:302-316.
32. Shu C., Quadra T.B.M.J. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology 2008, 349(1-2):31-43.
33. Sivakumar B. Suspended sediment load estimation and the problem of inadequate data sampling: a fractal view. Earth Surface Processes and landforms 2006, 31:414-427.
34. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on System, Man and Cybernetics 1985, 15(1):116-132.
35. Taniguchi T. Observation of sediment load in the river with the thiltmeter (with the thiltmeter of zollner suspension. International Association of Scientific Hydrology Bulletin 1958, 9:22-32.
36. Tayfur G. Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal 2002, 47(3):879-892.