The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP), radial basis function...
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The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP), radial basis function neural network (RBFNN), and support vector machine (SVM) models to forecast meteorological droughts in Iran. The spatial-temporal pattern of droughts in Iran was also found using recorded observation data from 1980 to 2014. A nomadicpeoplealgorithm (NPA) was utilized to train the ANFIS, MLP, RBFNN, and SVM models. Additionally, the NPA was benchmarked against the bat algorithm, salp swarm algorithm, and krill algorithm (KA). The hybrid ANFIS, MLP, RBFNN, and SVM models were used to forecast the 3-month standardized precipitation index. New evolutionary algorithms were utilized to improve the convergence speed of the soft computing models and their accuracy. First, random stations, namely, in Azarbayjan (northwest Iran), Khouzestan (southwest Iran), Khorasan (northeast Iran), and Sistan and Balouchestan (southeast Iran) were selected for the testing of the models. According to the results obtained from the Azarbayjan station, the Nash-Sutcliffe efficiency (NSE) was 0.93, 0.86, 0.85, and 0.83 for the ANFIS-NPA, MLP-NPA, RBFNN-NPA, and SVM-NPA models, respectively. For Sistan and Baloucehstan, the results indicated the superiority of the ANFIS-NPA model, followed by the MLP-NPA model, compared to the RBFNN-NPA and SVM-NPA models, and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models. The second aim of the study was to capture the relationship between large-scale climate signals and drought indices by using a wavelet coherence analysis. The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices.
Designing a routing protocol that can against the attacks of malicious nodes is very essential because open wireless channels make a wireless ad hoc network (WAN) affected by different security attacks. To overcome th...
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Designing a routing protocol that can against the attacks of malicious nodes is very essential because open wireless channels make a wireless ad hoc network (WAN) affected by different security attacks. To overcome this problem, Dual-Discriminator Conditional Generative Adversarial Network Optimized with Namib beetle optimizationalgorithm is proposed in this manuscript for cluster head based secure routing in Wireless Ad-hoc Networks (CH-SR-DDCGAN-NBOA-WAN). Here, the proposed CH-SR-DDCGAN-NBOA-WAN method consists of two phases: (i) to find the optimal CHs (ii) to find optimal trust path. At first, Dual-discriminator conditional generative adversarial network is utilized for selecting Cluster Head (CH) depending on the multi-objective fitness function. The multi-objective fitness function considers the factors, namely set of nodes, determining short security paths connecting sources and destinations, and links schedule with the routes that fulfills the request of all nodes to decrease the energy consumption of whole network, and also increase the defense against spiteful nodes. Therefore, in this work, proposed Namib beetle optimizationalgorithm (NBOA) is utilized for optimizing the Dual-discriminator conditional generative adversarial network, which selects the CH accurately. After CH selection, nomadic people optimization algorithm (NPOA) is used to identify the trust path from that are confirmed by using trust parameters, such as integrity, consistency, forwarding rate, availability factor. The proposed CH-SR-DDCGAN-NBOA-WAN approach is activated in network stimulator NS-2 and its performance is examined under performance metrics, such as drop, normalized network energy, network lifetime, delay, throughput, energy consumption, Packet Delivery Ratio. The proposed CH-SR-DDCGAN-NBOA-WAN approach attains 23.31%, 24.5%, and 30.01% lower packet drop, 12.45%, 17.34% and 24.67% higher network life time and 11.45%, 17.34% and 29.56% lower average delay time compared wit
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