​Fuzzy Logic Expert System for Taxonomic Variation of Solonchaks

Abstract

Background: The main objective of this research is to apply fuzzy logic to four Solonchaks, in order to determine their degree of remoteness or rapprochement with their central taxonomic concept. Therefore, we identify their possible seasonal taxonomic variation on the criteria established by World Reference Base (WRB). Methods: We have studied the seasonal evolution of salinity in a region of Algeria (Case of Rélizane), during two years 2012 and 2013 by applying fuzzy logic on the four soils. Result: The results reveal that the salinity increased during the dry period for all soils and it decreased during the wet period. On the taxonomic level, the application of fuzzy logic on the four soils revealed that the Solonchaks indices (Is) are always significantly higher than those of Calcisols indices (Ic). The four profiles have a similar behavior regarding the variation of Is. Indeed, when the salinity increases the soils come closer to the central taxonomic concept of the Solonchaks. Likewise, when the salinity decreases the soils move away from their central taxonomic concept. Consequently, they approach the central taxonomic concept of Calcisols. Thus, the variation of Isis closely related to the seasonal variation of salinity. Fuzzy logic, exhibited high precision concerning the membership value between soils over time. The application of fuzzy logic for other soil classifications in the world is possible.