Optimization of Maize Irrigation Schemes Based on Entropy-AHP Combination Weighting of TOPSIS Model

摘要

An improved algorithm is supported aiming at the repeat of indexical information in traditional TOPSIS method, and the subjectivity lacking and uncertain membership of entropy weighting method. First, build a standard decision matrix, and then combine and weight the evaluation index by using the method of entropy method and analytic hierarchy process (AHP). Second, replace the Euclidean distance computing method by relative entropy concept to compute the distance between under-evaluated object and ideal solution and negative ideal solution, and then compute the relative closeness degree between the evaluated object and the preferred option. The improvement of algorithm can not only avoid the evaluation result gotten through data analysis and without the subjectivity from the expert evaluation, but also solve the problem that the option close to the ideal solution is also approached to the negative ideal solution, which can improve the scientificity, rationality and practicability of TOPSIS model. For verifying the TOPSIS model after improvement can be used in the optimization choice of irrigation schemes, the influence to the maize water consumption and yield from different water treatments is analyzed by the field trial with the crop water consumption using the water balance method. The variation tendency of the calculated relative closeness degree is consistent with the tendency of maize field, and the two are in an exponential relationship, the correlation coefficient is 0.9984, and the study result can provide a theoretical basis to the optimization choice of maize irrigation schemes.