Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling

Water Resources Management - The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models...     

Improving the Summer Power Generation of a Hydropower Reservoir Using the Modified Multi-Step Ahead Time-Varying Hedging Rule

This paper aims to improve summer power generation of the Yeywa Hydropower Reservoir in Myanmar using the modified multi-step ahead time-varying hedging (TVH) rule as a case study. The results of the TVH rules were compared with the standard operation policy (SOP) rule, the binary standard operation policy (BSOP) rule, the discrete hedging (DH) rule, the standard hedging (SH) rule, the one-point hedging (OPH) rule, and the two-point hedging (TPH) rule. The Multi-Objective Genetic Algorithm (MOGA) was utilized to drive the optimal Pareto fronts for the hedging rules. The results demonstrated that the TVH rules had higher performance than the other rules and showed improvements in power generation not only during the summer period but also over the entire period.

     

El Niño-Southern Oscillation and Rainfall Variability in Central and Southern Sudan

Abstract

In this study, the effects of the El Niño-Southern Oscillation (ENSO) on the rainfall variability in the central (Savannah) and southern (Equatorial) regions of Sudan are examined. The annual rainfall data from 12 rainfall stations for 49 years are used in this examination. The results of the study show that the areal annual regionally averaged rainfall values in the two regions have decreased markedly since the early 1960s, with co-existence between the driest years and the warm ENSO events. The correlation between the annual regional rainfall values and the ENSO events is found to be relatively higher for the Savannah region than for the Equatorial region. Two regional ENSO-rainfall prediction models are developed, one for each region. These models use the ENSO sea surface temperature. The results of the models test show that both models can significantly improve the predictability of the annual rainfall values, which is essential for the planning and the management of water resources in Sudan     

Application of surrogate artificial intelligent models for real‐time flood routing

Developing a robust flood forecasting and warning system (FFWS) is essential in flood‐prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real‐time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run‐off hydrographs using the Hydrologic Engineering Center (HEC)‐1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC‐RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.     

PHYSICAL HABITAT ASSESSMENT IN URBAN RIVERS UNDER FUTURE FLOW SCENARIOS

River corridors in urban environments provide areas of biodiversity which are important for both aesthetic and economic reasons. A physical habitat model, which was used to assess urban rivers in Birmingham, UK, was applied to pairs of selected reaches to represent differing levels of habitat diversity on three rivers. The results for different life-stages of dace, roach and chub suggest that the worst physical habitat occurs in highly modified channels and at the highest flows. Four scenarios, which were designed to represent alterations in flow regime caused by changes in management practices, were calculated using the hydrological model. Changes in physical habitat created by changes to the flow regime were assessed using a consistent, replicable method. It was shown that an increase in runoff would have detrimental effects in all cases, and that less engineered sites would benefit more from flow reductions. The lack of a suitable habitat for fry is shown to be a limiting factor for fish at all sites.     

Retrofitting a stormwater retention pond using a deflector island

Stormwater retention ponds are one of the principal methods to treat stormwater runoff. Analysis of residence time distribution (RTD) curves can be used to evaluate the capability of these ponds for sediment removal. Deflector islands have been suggested as a means of improving the performance of retention ponds, due to their diffusing the inlet jet. In this study, the effect of an island on retention pond performance was investigated using a physical model of an existing stormwater retention pond. The physical model is a trapezoidal pond having top dimensions 4.1 x 1.5 x 0.23 m and side slopes of 2:1 (h:v). Three different arrangements were studied. The results show that placing an island to deflect the influent to a stormwater retention pond does not improve pond performance, rather it stimulates short-circuiting. This unexpected behaviour, in relation to previous studies, is considered to be a consequence of the model pond incorporating sloping walls; which is a novel aspect of this paper.     

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer withDeepLearning

Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.     

Countermeasures for local scour at offshore wind turbine monopile foundations: A review

Local scour at monopile foundations of offshore wind turbines is one of the most critical structural stability issues. This article reviews the contemporary methods of scour countermeasures at monopile foundations. These methods include armouring countermeasures (e.g., riprap protection) to enhance the anti-scour ability of the bed materials and flow-altering countermeasures (e.g., collars and sacrificial piles) to reduce downflow or change flow patterns around the monopiles. Stability number and size-selection equations for riprap armour layers are summarised and compared. Moreover, other alternative methods to riprap are briefly introduced and presented. A typical graph of the scour depth reduction with different collar sizes and elevations under specific test conditions is summarised and compared with a plot for a pile founded on a caisson. Reduction rates for different flow-altering countermeasures, including the collar, are listed and compared. A newly developed soil improvement method, namely microbially induced calcite precipitation (MICP), is also reviewed and introduced as a scour protection method. As a popular bio-soil treatment method, MICP has a good potential as a scour countermeasure method. Bio-soil treatment methods and traditional armouring methods are defined as active and passive soil enhancement scour countermeasures, respectively.