Artificial Intelligence Based IoT Framework For Natural Hazard Forecasting

Conventional assessment of natural hazards is limited to examination of a single aspect of hazard (such as landslides, earthquakes, hurricanes), using a combination of techniques: data collection followed by data analysis. Investigations of each form of natural hazard have developed their own hazard-specific methodologies, to carry out their probes. As a consequence, the different disciplines of natural hazard science use the same sources, with redefined nomenclature . With rapid advances in the domains of IoT, artificial intelligence and big data analytics, few research disciplines have put in place measures to adapt the data, collected from other disciplines, into their own field of study. In this paper, we introduce a futuristic IoT framework, which provides deeper \understanding of the science of landslide. This IoT framework manages data integration, communication, and combined interpretation from multiple platforms, with varying spatial and temporal resolutions. The prospective options of extending the data collected by this conceptual framework for assessment of other natural hazards are also presented. The future context of how progressive developments in data science can effectively drive systematic assessments of natural hazards is also presented.