Subsurface drainage performance study using SALTMOD and ANN models

Agricultural Water Management

Volumn 84, Issue 3, 2006, Pages 240-248

  Sarangi, A ^a   Singh, Man ^a   Bhattacharya, A K ^a   Singh, A K ^a  

^a INDIAN AGRICULTURAL RESEARCH INSTITUTE   (India)

Author keywords

Artificial neural network; Drainage effluent salinity; MARS; Root zone soil salinity; SALTMOD; Subsurface drainage

Indexed keywords

CROPS; DISCHARGE (FLUID MECHANICS); DRAINAGE; IRRIGATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

DRAINAGE EFFLUENT SALINITY; MARS; ROOT ZONE SOIL SALINITY; SALTMOD; SUBSURFACE DRAINAGE;

AGRICULTURE;

AGRICULTURE; CROPS; DISCHARGE (FLUID MECHANICS); DRAINAGE; IRRIGATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; DRAINAGE; EFFLUENT; PADDY FIELD; RHIZOSPHERE; SALINE SOIL; SALINITY;

AGRICULTURE; DISCHARGE; DRAINAGE; FARM CROPS; IRRIGATION; MATHEMATICAL MODELS; NEURAL NETWORKS;

ANDHRA PRADESH; ASIA; EURASIA; INDIA; SOUTH ASIA;

EID: 33745990754     PISSN: 03783774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agwat.2006.02.009     Document Type: Article

References (27)

1. Abraham A., and Steinberg D. Is neural network a reliable forecaster on earth? A MARS query. In: Jose M., and Alberto P. (Eds). Bio-Inspired Applications of Connectionism, Lecture Notes in Computer Science vol. 2085 (2001), Springer-Verlag, Germany, Spain 679-686
2. ASCE. Artificial neural networks in hydrology. II. Hydrologic applications. ASCE J. Hydrol. Eng. 5 2 (2000) 124-137
3. Bhattacharya A.K. Drainage of agricultural lands. In: Singh G.B., and Sharma B.R. (Eds). 50 Years of Natural Resource Management Research (1999), Division of Natural Resource Management, ICAR, Krishi Bhavan, New Delhi, India 347-362
4. Chen S., Cowan C.F., and Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks 2 (1991) 302-309
5. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 19 (1991) 1-141
6. Huang Z., and Williamson M.A. Geological pattern recognition and modeling with a general regression neural network. Can. J. Explor. Geophys. 30 1 (1994) 60-68
7. James l.D., and Burgess S.J. Selection, calibration and testing of hydrologic models. In: Haan C.T., Johnson H.P., and Brakensiek D.L. (Eds). Hydrological Modeling of Small Watersheds (1982), American Society of Agricultural Engineers, St. Joseph, MI 215-257
8. Kim B., Kim S., and Kim K. Modelling of plasma etching using a generalized regression neural network. Vaccum 71 (2003) 497-503
9. Oosterbaan R.J. SALTMOD ver 1.1: Description of Principles and Applications (1998), ILRI, Wageningen, The Netherlands 106 pp
10. Oosterbaan R.J., and Abu Senna M. Using saltmod to predict drainage and salinity in the Nile Deltas. Annual Report (1989), ILRI, Wageningen, The Netherlands pp. 63-74
11. Patel R.M., Prasher S.O., Goel P.K., and Bassi R. Soil salinity prediction using artificial neural networks. J. Am. Water Resour. Assoc. 38 1 (2002) 91-100
12. Salehi F., Prasher S.O., Amin S., Madani A., Jebelli S.J., Ramaswamy H.S., Tan C., and Drury C.F. Prediction of annual nitrate-N losses in drain outflows with artificial neural networks. Trans. ASAE 43 5 (2000) 1137-1143
13. Sarangi A., and Bhattacharya A.K. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agric. Water Manage. 78 (2005) 195-208
14. Sharma V., Negi S.C., Rudra R.P., and Yang S. Neural networks for predicting nitrate-nitrogen in drainage water. Agric. Water Manage. 63 (2003) 169-183
15. Shukla M.B., Kok R., Prasher S.O., Clark G., and Lacroix R. Use of artificial neural networks in transient drainage design. Trans. ASAE 39 (1996) 119-124
16. Singh M., Bhattacharya A.K., Nair T.V.R., and Singh A.K. Nitrogen losses through subsurface drainage effluent in coastal rice fields from India. Agric. Water Manage. 52 (2002) 249-260
17. Singh M., Bhattacharya, Singh A.K., and Singh A. Application of SALTMOD in coastal clay soils in India. Irrigation Drainage Syst. 16 (2002) 213-231
18. Sivakumar B., Jayawardena A.W., and Fernando T.M.K.G. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J. Hydrol. 265 (2002) 225-245
19. Sudheer K.P., Gosain A.K., and Ramasastri K.S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process. 16 (2002) 1325-1330
20. Tsoukalas L.H., and Uherg R.E. Fuzzy and Neural Approaches in Engineering (1996), John Wiley & Sons Inc., New York pp. 196-267
21. Tyagi N.K., Tyagi K.C., Pillai N.N., and Willardson L.S. Decision support for irrigation system improvement in saline environment. Agric. Water Manage. 23 4 (1993) 285-301
22. Yang C.C., Prasher S.O., and Lacroix R. Applications of artificial neural networks to land drainage engineering. Trans. ASAE 39 (1996) 525-533
23. Yitian L., and Gu R.R. Modeling flow and sediment transport in a river system using an artificial neural network. Environ. Manage. 31 1 (2003) 122-134
24. Yu C., Northcott W.J., and McIsaac G.F. Development of an artificial neural network model for hydrologic and water quality modeling of agricultural watersheds. Trans. ASAE 47 1 (2004) 285-290
25. Zhang B., and Govindaraju R. Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds. J. Hydrol. 273 (2003) 18-34
26. Zhang G.P. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybernetics C: Appl. Rev. 30 (2000) 451-462
27. Zhang Z.X., and Kushwaha R.L. Applications of neural networks to simulate soil-tool interaction and soil behavior. Can. J. Agric. Eng. 41 (1999) 119-125