algorithm genetic

Due to limited water resources in Iran, the optimal use of water resources and improvement of water use efficiency is necessary, especially in agriculture. In current work, cropping pattern optimization was carried out in Mehran Plain of Ilam Province based on water resources, cultivated area and biodiversity constraints using genetic algorithm. The optimization model was applied to three different scenarios based on a combination of different constraints. The results showed that the cropping pattern in 2016-17 was not optimal and the biodiversity index was low. The resulting profit and biodiversity in all scenarios are higher than the current situation in Mehran plain. The amount of profit increase in combinations one, two and three is 70, 101 and 132% higher than the profit of the existing cropping pattern, respectively, and in terms of biodiversity, the Shannon-Wiener criterion is more than twice as high as the Shannon-Wiener criterion in the existing simple cropping pattern in all scenarios. Wheat, canola, sesame and okra are strongly represented in most optimal cropping patterns. Wheat has the largest acreage and tomato and alfalfa and corn have the least acreage due to the low profit and high water consumption of these products. Crops such as corn, sesame, okra and cucumber are strongly represented in the optimal cropping patterns and can be used as alternatives to the current crops to increase agricultural profits.

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.