Water Resources Management and Modeling

Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Analysis and Synthesis of Magnetically Negative (MNG) Material using Softcomputing Techniques

Unique properties of Metamaterial are widely used in Electromagnetic Engineering, and the metamaterial has gained significant attention to be a major research area. Some of its recent research areas are carpet cloaking and metasurface design. The unique properties of these materials include simultaneous negative electromagnetic property, i.e., both permeability and permittivity are negative, because of which a negative refractive index is generated.Thus there are three primary classes of metamaterials. When only the permittivity is negative, the material is called ENG (Electrical Negative). Similarly material with only negative permeability is known as MNG (Magnetic Negative). Further when both are negative the material is regarded as DNG (Double Negative). Out of these three, the analysis and synthesis of MNG is very complicated and difficult. Therefore, the focus in this work is only on MNG, and the word "metamaterial" refers to MNG unless otherwise mentioned specifically. These type of materials don’t occur in nature and hence manufactured by making array of small unit cells of specific structure(s) made up of conductors. Although the concept of the existence of negative refractive index was proposed in the 1960s by Veselago, it took around 40 years to be verified practically when smith et al. did the experiment in 2001. They used an array of unit cell structures as Split-Ring-Resonators (SRR) and thin wires to verify the concept. Thereafter researchers are working to develop different forms of metamaterial unit cells and for which metamaterial is still an open area of research. However, while designing a metamaterial unit cell, absence of an empirical formula makes the model analysis and synthesis difficult. Although with the help of EM simulation tools this is possible, it usually is too difficult, time consuming and costly. Due to this researchers are motivated to look for alternative methods. In this work, some techniques to develop CAD models are presented based on soft computing techniques for metamaterial analysis and synthesis. Use of different soft computing techniques in the field of microwave engineering is documented in the literature. However, unconventional unit cell structures are difficult to analysis because of unavailability of predefined mathematical formulas and equivalent analysis. This can be done by the complex Modified Nicolson-Ross-Weir (NRW) method with the support of EM simulation tools which are expensive. Frequency dependency of metamaterial characteristics for any kind of unit cell structure follows a similar pattern which is obtained from Lorentz model. The basic idea in this work, which develops CAD Models for metamaterial unit cell of unconventional structures is based on the assumption that each type of unit cell can be mapped to an equivalent SRR structure, for which empirical formula is available. This is done by implementing the concept of Space Mapping technique or surrogate based modeling. Most important contribution of the work is the development of Space Mapped CAD model for analysis of an Ω atom. The developed model is validated with a Deformed-Ω atom, which is developed by integrating the concept of Space Mapping (SM) and Artificial Neural Network. Thereafter, the work progresses with proposing CAD models for synthesis of SRR. The objective is to find the design parameters of SRR for a desired material characteristic and frequency. With the availability of only a complex non-linear analysis formula, the synthesis becomes a reverse engineering problem, which is difficult to process. Three different models are proposed to solve the problem. The first approach is use of Inverse Artificial Neural Network concept, which uses a trained neural network (IANN) to perform output-to-input mapping. The developed CAD model using this approach includes integration of three concepts: IANN, Prior Knowledge Input-Difference (PKI-D) and SM. Although the model is capable of synthesizing a metamaterial unit cell, still it has some disadvantages. To overcome the disadvantages (such as lower convergence rate, lower accuracy and complex programming), use of Evolutionary Algorithms (Genetic Algorithm and Differential Evolution) is proposed. While developing CAD model based on EA, the methodology is first tested by synthesizing Rectangular Microstrip Antenna (RMPA) and then using the same concept, an SRR is synthesized. A comparison shows DE based model to be more efficient than IANN and GA based models in terms of convergence speed, accuracy and robustness

Towards COP27: The Water-Food-Energy Nexus in a Changing Climate in the Middle East and North Africa

Due to its low adaptability to climate change, the MENA region has become a "hot spot". Water scarcity, extreme heat, drought, and crop failure will worsen as the region becomes more urbanized and industrialized. Both water and food scarcity are made worse by civil wars, terrorism, and political and social unrest. It is unclear how climate change will affect the MENA water–food–energy nexus. All of these concerns need to be empirically evaluated and quantified for a full climate change assessment in the region. Policymakers in the MENA region need to be aware of this interconnection between population growth, rapid urbanization, food safety, climate change, and the global goal of lowering greenhouse gas emissions (as planned in COP27). Researchers from a wide range of disciplines have come together in this SI to investigate the connections between water, food, energy, and climate in the region. By assessing the impacts of climate change on hydrological processes, natural disasters, water supply, energy production and demand, and environmental impacts in the region, this SI will aid in implementation of sustainable solutions to these challenges across multiple spatial scales