Application of the Fuzzy Sets Theory and FAO Method on Suitability and Clustering of Land Units in Marand region for Sunflower and Canola Products

Document Type : Research Paper

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Abstract

In recent decades, increasing of population and limitation of arable lands have led to consider production level increasing
and land suitability assessment. Several methods such as limitation (Maximum, Number and intensity), parametric (Story,
square root) and a new fuzzy logic based methods have been developed to assess the suitability of lands. Classical methods
of land suitability assessment are not able to express the continuous nature of soil properties and define the land suitability
classes as non-collectible. Therefore, using of the fuzzy sets in studies of land suitability assessment can lead to a better
understanding of them. Also fuzzy clustering and kohonen artificial neural network can be used for homogenous soils
determination and requirements data completion. In this study, for comparison of the fuzzy set theory and FAO method,
calculated correlation coefficients values between the land index and yield of the sunflower and canola by fuzzy method
were (0.709 and 0.617, respectively), more than those with the Parametric method (0.709 and 0.617, respectively).These
results demonstrate the high accuracy and potential of the fuzzy logic method in relation to the classical methods of land
suitability assessment, and can be described by the capability of the fuzzy logic in expressing the continuous nature of the
processes. Obtained results showed that representative profiles of 1 and 4 were homogenous based on landform clustering
methods and these results could be used for information completion.

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Water and Soil Science
Volume 26, 1-1 - Serial Number 2
June 2016
Pages 273-2902

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