Early Warning Systems for Natural Disasters

EasyChair Preprint 14565

Abstract

Early Warning Systems (EWS) for natural disasters play a crucial role in minimizing the loss of lives and reducing damage to infrastructure by providing timely and accurate information on impending threats. These systems integrate technological innovations, real-time data collection, and predictive modeling to forecast natural hazards such as earthquakes, tsunamis, hurricanes, floods, and volcanic eruptions. EWS are composed of four key components: risk knowledge, monitoring and warning services, dissemination and communication, and response capability. Effective EWS require a collaborative effort between governments, scientific communities, and local populations to ensure that alerts are translated into actionable steps that protect vulnerable populations. The success of EWS hinges not only on the precision of hazard detection but also on the social preparedness and education of communities at risk. Enhancing early warning systems with improved technology, such as AI-driven predictive tools and enhanced satellite monitoring, could further reduce the impacts of natural disasters globally. This abstract discusses the current state, challenges, and future opportunities in the development of robust EWS to safeguard human lives and properties.

Keyphrases: AI-driven, Early Warning Systems (EWS), natural disasters

@booklet{EasyChair:14565,
  author    = {Favour Olaoye and Axel Egon},
  title     = {Early Warning Systems for Natural Disasters},
  howpublished = {EasyChair Preprint 14565},
  year      = {EasyChair, 2024}}