Mohammad Ali Izadbakhsh; Reza Hajiabadi
Abstract
In the article, through the adaptive neuro-fuzzy inference system (ANFIS), a sensitivity analysis is conducted on the variables affecting the discharge capacity of the weir. To this end, the variables affecting the discharge capacity of labyrinth weirs are initially identified. Then, using these input ...
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In the article, through the adaptive neuro-fuzzy inference system (ANFIS), a sensitivity analysis is conducted on the variables affecting the discharge capacity of the weir. To this end, the variables affecting the discharge capacity of labyrinth weirs are initially identified. Then, using these input parameters, seven ANFIS models are developed for conducting the sensitivity analysis. After that, the most optimal membership function number for the ANFIS model is chosen. In other words, by conducting the trial and error process, the best number of the membership functions in terms of time and modeling accuracy are selected. Then, the sensitivity analysis is performed for the ANFIS models and the superior ANFIS model is chosen finally. The accuracy of the superior model in both the validation and testing artificial intelligence (AI) methods is in an acceptable range. For example, the scatter index (SI), correlation coefficient (R) and the Nash-Sutcliff efficiency coefficient (NSC) for the model in the testing mode are obtained 0.049, 0.964 and 0.924, respectively. It should be noticed that the outcomes of the sensitivity analysis show that the ratio of the weir head to the weir crest and the Froude number are introduced as the most effective input parameters. Eventually, a computer code is proposed to estimate the discharge capacity of labyrinth weirs by this model.
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.
