Artificial Neural Network Model for Subsurface-Drained Farmlands
Publication: Journal of Irrigation and Drainage Engineering
Volume 123, Issue 4
Abstract
This paper describes the development of an artificial neural network (ANN) model to simulate fluctuations in midspan water-table depths and drain outflows, as influenced by daily rainfall and potential evapotranspiration rates. Unlike conventional models, ANN models do not require explicit relationships between inputs and outputs. Instead, ANNs map the implicit relationship between inputs and outputs through training by field observations. Compared with conventional models, the ANN model requires fewer input parameters since the inputs that remain constant are not considered by ANNs. Therefore ANNs can be executed quickly on a microcomputer. These benefits can be exploited in the real-time control of water-table management systems. The model was developed using field observations of water-table depths from 1991 to 1993 and drain outflows from 1991 to 1994 made at an agricultural field in Ottawa, Canada. The root mean squared errors and standard deviation of errors of simulated results were found to range from 46.5 to 161.1 mm and 46.6 to 139.2 mm, respectively, thus showing potential applications of ANNs in land drainage engineering.
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References
1.
Belmans, C., Wesseling, J. G., and Feddes, R. A.(1983). “Simulation model of the water balance of a cropped soil: SWATRE.”J. Hydro., Amsterdam, The Netherlands, 63, 271–286.
2.
Bengtson, R. L., Garzon, R. S., and Fouss, J. L.(1993). “A fluctuating watertable model for the management of a controlled-drainage/subirrigation system.”Trans., ASAE, 36(2), 437–443.
3.
Doty, C. W., Cain, K. R., and Farmer, L. J. (1983). “Considerations for the design and operation of control drainage-subirrigation (CD-SI) systems.”ASAE Paper No. 83-2566. St. Joseph, Mich.
4.
Fouss, J. L., Rogers, J. S., and Carter, C. E.(1989). “Sump-controlled water table management predicted with DRAINMOD.”Trans. ASAE, 32(4), 1303–1308.
5.
Fouss, J. L., Skaggs, R. W., and Rogers, J. S.(1987). “Two-stage weir control of subsurface drainage for water table management.”Trans. ASAE, 30(6), 1713–1719.
6.
Friend, C.(1996). “Even aircraft have feelings.”New Sci., 149(2015), 32–35.
7.
Haykin, S. (1994). Neural networks: a comprehensive foundation. Macmillan/McGraw-Hill School Publishing Co., Inc., New York, N.Y.
8.
Kao, J.-J.(1996). “Neural net for determining DEM-based model drainage pattern.”J. Irrig. and Drain. Engrg., ASCE, 122(2), 112–121.
9.
McClendon, R. W., Hoogenboon, G., and Seginer, I.(1996). “Optimal control and neural networks applied to peanut irrigation management.”Trans. ASAE, 39(1), 275–279.
10.
Mitchell, B., and Shrubsole, D. (1994). Canadian water management: visions for sustainability. Canadian Water Resources Association, Cambridge, Ontario, Canada.
11.
Neural computing: a technology handbook for Professional II/PLUS and NeuralWorks Explorer. (1993). NeuralWare, Inc., Pittsburgh, Pa.
12.
Reference guide: software reference for Professional II/PLUS and NeuralWorks Explorer. (1993). NeuralWare, Inc., Pittsburgh, Pa.
13.
Sabbagh, G. J., Fouss, J. L., and Bengtson, R. L.(1993). “Comparison of EPIC-WT and DRAINMOD simulated performance of land drainage systems.”Trans. ASAE, 36(1), 73–79.
14.
Sanoja, J., Kanwar, R. S., and Melvin, S. W.(1990). “Comparison of simulated (DRAINMOD) and measured tile outflow and water table elevations from two field sites in Iowa.”Trans. ASAE, 33(3), 827–833.
15.
Seginer, I., Y. Hwang, Boulard, and T., and Jones(1996). “Mimicking an expert greenhouse grower with a neural-net policy.”Trans. ASAE, 39(1), 299–306.
16.
Shady, A. M. (1989). Irrigation drainage and flood control in Canada. Canadian International Development Agency, Hull, Quebec, Canada.
17.
Shukla, M. B., Prasher, S. O., Madani, A., and Gupta, G. P.(1994). “Field validation of DRAINMOD in Atlantic Canada.”Can. Agric. Engrg., Saskaton, Canada, 36(4), 205–213.
18.
Shukla, M. B., Kok, R., Prasher, S. O., Clark, G., and Lacroix, R.(1996). “Use of artificial neural networks in transient drainage design.”Trans. ASAE, 39(1), 119–124.
19.
Singh, M., Prasher, S. O., Tan, C. S., and Tejawat, C. M.(1994). “Evaluation of DRAINMOD for southern Ontario conditions.”Can. Water Resour. J., Waterloo, Canada, 19(4), 313–326.
20.
Skaggs, R. W. (1980). DRAINMOD: reference report. North Carolina State University, Raleigh, N.C.
21.
Skaggs, R. W., Workman, S. R., Parsons, J. E., and Rice, J. (1990). DRAINMOD: user's manual. North Carolina State University, Raleigh, N.C.
22.
Supplement for NeuralWorks Professional II/PLUS v 5.2 and NeuralWorks Explorer v 5.2. (1995). NeuralWare, Inc., Pittsburgh, Pa.
23.
Thooko, L. W., Rudra, R. P., Dickinson, W. T., Patni, N. K., and Wall, G. J.(1994). “Modeling pesticide transport in subsurface drained soil.”Trans. ASAE, 37(4), 1175–1181.
24.
Workman, S. R., and Skaggs, R. W.(1989). “Comparison of two drainage simulation models using field data.”Trans. ASAE, 32(6), 1933–1938.
25.
Yang, C.-C., Prasher, S. O., and Lacroix, R. (1996a). “Application of artificial neural networks in subsurface drainage system design.”Proc., Comp. in Agr., ASAE, St. Joseph, Mich., 932–940.
26.
Yang, C.-C., Prasher, S. O., and Lacroix, R.(1996b). “Applications of artificial neural networks to land drainage engineering.”Trans. ASAE, 39(2), 525–533.
27.
Yang, C.-C., Prasher, S. O., and Lacroix, R.(1996c). “Applications of artificial neural networks to simulate water-table depths under subirrigation.”Can. Water Resour. J., 21(1), 27–44.
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Copyright © 1997 American Society of Civil Engineers.
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Published online: Jul 1, 1997
Published in print: Jul 1997
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