Abstract
Multi Attribute Utility Theory (MAUT) is employed to rank the irrigation subsystems of Mahi Bajaj Sagar Project, Rajasthan, India. Seven performance evaluation criteria, namely, land development works, timely supply of inputs, conjunctive use of water resources, participation of farmers, economic impact, crop productivity and environmental conservation are employed. Kohonen Artificial Neural Networks (KANN) is employed to classify the irrigation subsystems that can be utilized for further ranking by MAUT. Spearman rank correlation technique is employed to compute correlation coefficient values between the obtained ranking pattern. Sensitivity analysis studies are also made to check the robustness in ranking. The proposed methodology can be applied for similar situations.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call