Yahya Choopan; Somayeh Emami
Abstract
Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and ...
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Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. In this study, new methods based on the election algorithms (EA) and gray wolf optimizer (GWO) algorithms were used to determine the optimal cultivation pattern in Moghan plain during the statistical years of 2007-2016. The objective function in the agricultural sector was based on each product and its yield, net from each product and the cultivar. Then, maximization of the objective function was performed using GWO and EA algorithm. The results of using GWO algorithm in determining the optimal crop pattern in Moghan plain showed that using economic policies such as changing the cultivar pattern, we can obtain a better result compared to EA algorithm in the agricultural sector. In general, the results of GWO algorithm showed that in the Moghan plain with 0.9, 140 billion rials, that is, about 42 % will have economic growth. In sum, the results showed that GWO algorithm with high values of statistical criteria (R2=0.96, RMSE=0.022 and NSE=0.75) has a higher efficiency in optimizing the crop cultivation pattern of Moghan plain, which can be applied to the correct planning for other cultivation areas to be employed.
Yahya Choopan; Somayeh Emami
Abstract
In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial ...
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In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial wastewater (sugar factory wastewater), a combination of well water and wastewater in two levels (complete irrigation and irrigation with 75 % water stress) and soil characteristics of area were used as input parameters. To achieve this goal, based on the number of data and inputs, 200 barley field experiments data set were used, of which 80 % (160 data) was used for the training and 20 % (40 data) for the testing the network. The results showed that RBF model has high potential in estimating barley yield with Levenberg Marquardt training and 4 hidden layers. Also the values of statistical parameters R2 and RMSE were 0.81 and the 33.12, respectively. In general, the results showed that ANNs model is able to better estimate the barley yield when irrigation water level parameter with well water is selected as input.
