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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

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 noncommensurable scales.

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.

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