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Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention outlines how nanotechnology and space technology could be applied for the detection of disaster risks in early stages, using cheap sensors, cheap constellations of low Earth orbit (LEO) satellites, and smart wireless networks with artificial intelligence (AI) tools. Nanomaterial-based sensors (nanosensors) can offer several advantages over their micro-counterparts, such as lower power or self-powered consumption, high sensitivity, lower concentration of analytes, and smaller interaction distances between the object and the sensor. Besides this, with the support of AI tools, such as fuzzy logic, genetic algorithms, neural networks, and ambient intelligence, sensor systems are becoming smarter when a large number of sensors are used. This book is an important reference source for materials scientists, engineers, and environmental scientists who are seeking to understand how nanotechnology-based solutions can help mitigate natural disasters.
Nanotechnology for disaster prevention
Application of nanotechnology in disaster prevention: An introduction
Nanomaterials for construction building products designed to withstand natural disasters
Nano-sensors and nano-devices for biological disaster monitoring (virus/disease epidemics/animal plagu
Internet of Things-based disaster management system
Nanosensors for smartphone-enabled sensing devices
Smart and autonomous (self-powered) nanosensor networks
Nanosensors for smartphone sensing method
Space technology for disaster prevention
The role of satellite remote sensing in natural disaster management
The synergy of remote sensing and geographical information systems in the management of natural disasters
Small satellites for disaster monitoring
A comparative study of deep learning-based time-series forecasting techniques for fine-scale&ampspi
Satellite and aerial remote sensing in disaster management: An introduction
Emerging role of unmanned aerial vehicles (UAVs) for disaster management applications
Smart remote sensing network for early warning of disaster risks