Optimization of echo state networks for drought prediction based on remote sensing data

In this paper, we used echo state networks - a class of recurrent neural networks - for prediction of drought based on remote sensing data. To this end, the drought index was obtained for a number of stations in different clime zones of Iran. For each station, we also extracted the corresponding vegetation indices based on satellite imagery. Our model takes the satellitebased features as input and outputs the severity of drought. One of the major challenges of echo state networks is optimization of the reservoir parameters. Here we used a method based on Kronecker product in order to substantially reduce the parameter space to be optimized. We then used various optimization techniques including genetic algorithms, simulated annealing and differential evolution. Our results show that the method based on differential evolution results in the best performance as compared to others

Optimized echo state networks for drought modeling based on satellite data

Remotely sensed data obtained through satellite imaging is a useful tool for modeling environmental phenomena such as drought. In this manuscript, we apply optimized echo state networks to model and predict drought severity based on satellite images. To this end, a model is constructed in which the satellite-based vegetation index is fed as an input and drought severity index is obtained as output. We use a Kronecker-based approach to reduce the number of parameters of echo state networks to be optimized (i.e., the internal weights of reservoir). A number of evolutionary algorithms are used to optimize the parameters, of Differential Evolution results in the best performance as compared to genetic algorithms and simulated annealing. The proposed model also outperforms neural network models including multilayer perceptrons, radial basis function networks and support vector machines. © 2015 ICIC International

Effects of Nisin, Chitosan and extract of Eryngo on the shelf life of rain bow trout (Oncorhynchus mykiss) roe during refrigerated storage (4±1 ̊C)

The aim of this study was to determine the effects of nisin (250 international units per gram), chitosan (1%), eringo extract (1%), combination of three material and salt treatment (1.5%) on the quality and sensorial traits of rain bow trout (Oncorhynchus mykiss) roe during refrigerated (4±1 ̊C) storage for 60 days. Lipid, ash, moisture, pH, amount of TVN, TBA, Aerobic mesophilic bacteria, psychrotrophic bacteria, yeast and mold were evaluated at 0, 15, 30, 45 and 60 days. Sensorial analyses of samples were evaluated. The results showed that Chitosan and combinational treatment had significant effect (p≤ 0.05) in decrease of microorganisms after 45 days. In addition, TVN had a significant increase during the time (p≤0.05) but in Chitosan and combinational treatment not observed significant change until 45 days. According to the sensory evaluation, was not significant difference between the chitosan and combination treatments until 45 days. Based on the results of chemical and biological analyses, control, salt, nisin and E.extract were unusable between 0 and 15 days. Therefore, it can be concluded that the use of chitosan improved shelf life and organoleptic properties of fish roes effectively