Raju, KS and Kumar, DN and Duckstein, L (2006) Artificial neural networks and multicriterion analysis for sustainable irrigation planning. In: Computers & Operations Research, 33 (4). pp. 1138-1153.
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The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||Irrigation planning; Linear programming; Kohonen neural network; Multicriterion analysis.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||20 Oct 2010 05:09|
|Last Modified:||20 Oct 2010 05:09|
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