Machine Learning and Space Technology Solutions for Climate Change Adaptation
摘要
Climate change adaptation is critical to sustainability across social and geographical scales. Many actions that facilitate adaptation to climate change are executed to deal with current extreme events such as heat waves, floods,and cyclones. In industries, it is essential to adapt to sustainable business practices. Many times, planned adaptation initiatives are also not executed as stand-alone measures but are embedded within broader sectoral initiatives such as water resource planning, coastal defense, disaster management planning, and green engineering. As we move towards sustainable social networks across the world, geographically referenced location data becomes important. Micro-satellite constellations are providing continuous (real-time) and more economical inputs than large satellites for monitoring climate change adaptation. Even the ICT-with Industry 5.0 becomes a kingpin by adopting all that is advancing technically (both in AI and Quantum) and to put human in the loop. The carbon nanotubes (CNT) and polymer-based design and fabrication of nanosensors, S-band interferometric synthetic aperture radar payloads, for earth observation, communication payloads and space electric propulsion systems that can adapt to electromagnetic interference while orbiting are the various space materials that can bring economically sustainable solutions to studying climate change and geohazards. Spatial inputs from microsatellites around the world and Indian and global webportals assist in modeling solutions for natural resources and waste management using big-geodata analytics and spatial machine learning tools. The international engineering curriculum design for the common problems facing society, such unequal distribution of energy, water, minerals, forests, and climate change, is also discussed.