Ranking of Sprinkler Irrigation Systems Using Hybridizing of Multi-Attribute Decision-Making Approaches of Entropy, Grey Relational, and TOPSIS (EGC-TOPSIS): Dehgolan Plain

Today, appropriate selection of irrigation systems in farms is necessary for increasing water use efficiency. The selection of irrigation systems in different regions is complex problem and involves different factors and levels of decision making. In the present study, by hybridizing multi-attribute decision-making (MADM) approaches of Entropy, grey relational, and TOPSIS; an integrated method of EGC-TOPSIS were applied for appropriate selection of sprinkler irrigation systems based on performance evaluation indicators of irrigation systems. Then, the results of hybrid EGC-TOPSIS method were compared with E-TOPSIS method. Also, the hybrid principal component analysis-multiple linear regression (MLR-PCA) model was used for identify the estimation equations of ultimate decision index and distance closeness index for hybrid EGC-TOPSIS and E-TOPSIS methods, respectively. For this aim, 20 sprinkler irrigation systems including movable sprinkler solid-set and wheel-move irrigation systems were evaluated in Dehgolan plain as case study. The evaluation was done by single sprinkler approach and using indicators of Christensen uniformity coefficient, distribution uniformity, water application efficiency in the lower quarter, potential efficiency of lower quarter, deep percolation losses, and wind drift and evaporation losses. The entropy results show that the distribution uniformity with weight of 0.194 is the most effective factor for ranking of studied irrigation systems. The mean ultimate decision index and mean ranks of wheel-move irrigation systems were calculated equal to 0.52 and 9.1, respectively, that show the superiority of this system than movable sprinkler solid-set system in the studied region. This result was approved by cluster analysis method. The ranking results of both hybrid EGC-TOPSIS and E-TOPSIS models were significantly correlated with R2=0.79. Also, the MLR-PCA model with three main components was appropriately able to estimate ultimate decision index by R2=0.95. Based on the results, the hybrid EGC-TOPSIS method with strong mathematical background can produce useful, comprehensive, and practical results that can be utilized for evaluating and ranking of irrigation systems in similar regions.