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Fuzzy Modeling for Performance Assessment of Irrigation Systems
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Pp. 826-842 in Proceedings of the World Congress of Computers in Agriculture and Natural Resources (13-15, March 2002, Iguacu Falls, Brazil) 701P0301.(doi:10.13031/2013.8417)
Authors: Hani Nabhan Sewilam and Fritz G. Rohde
Keywords: Fuzzy Logic, Performance Indicators, Performance Assessment, Irrigation Management
In developing countries, many irrigation systems are performing far below their
technical and financial potentials. Therefore, performance assessment of irrigation
systems is a problem of increasing concern among policy makers to answer
questions such as isis our system improving or deteriorating over time?lo. Generally,
performance indicators are used for this purpose. However, aggregating them to
draw an overall conclusion is a complex task due to associated uncertainties and
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In the current work, nine indicators are used to assess the performance of crop
production, water delivery and financial aspects. A three-level hierarchical structure
is adopted to formulate the interrelationship of the indicators. A computer-based
model that relies on fuzzy set theory was developed to demonstrate the hierarchical
aggregation with the nine indicators as inputs and one overall indicator as an output.
The proposed model employs three main concepts: absolute rating, relative rating
and stepwise aggregation of indicators. Absolute rating is performed through
pre-defined membership functions to fuzzify the estimated values of input
indicators. The fuzzified values are then relatively rated and aggregated to obtain
fuzzy values that describe the output indicator. Finally, a defuzzification process is
performed to transform fuzzy outputs to a single crisp value that reflects the overall
performance of the system.
The model was used to compare the overall performance of six irrigation systems in
different countries. It shows ability to; (a) handle the associated uncertainty within a
strict mathematical framework, (b) aggregate indicators with noncommensurate
scales, and (c) flexibly accept linguistically described indicators.