Comprehensive benefit analysis of geographic information monitoring and early warning system for disaster prevention and reduction in rural areas: simulation experiment based on system dynamics

Fan Hu

doi:10.1117/12.2667326

10 February 2023 Comprehensive benefit analysis of geographic information monitoring and early warning system for disaster prevention and reduction in rural areas: simulation experiment based on system dynamics

Fan Hu

Author Affiliations +

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125520W (2023) https://doi.org/10.1117/12.2667326
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Background: The geographic information monitoring and early warning system can effectively warn the relevant natural disaster information in advance and help rural areas reduce the loss of natural disasters. Existing research generally focuses on the technical research of geographic information monitoring and early warning system, which lacks a comprehensive analysis of the comprehensive benefits of disaster prevention and mitigation in rural areas. Methods: A comprehensive evaluation model of system dynamics was constructed, and the comprehensive benefit relationship between geographic information monitoring and early warning system and rural disaster prevention and mitigation was analyzed. Results: Through the construction of a multi-dimensional dual-element system dynamics model, it was found that the geographic information monitoring and early warning system can effectively help improve the comprehensive benefits of disaster prevention and mitigation in rural areas.

Citation Download Citation

Fan Hu "Comprehensive benefit analysis of geographic information monitoring and early warning system for disaster prevention and reduction in rural areas: simulation experiment based on system dynamics", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125520W (10 February 2023); https://doi.org/10.1117/12.2667326

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