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Research on Migration Risk of Island Countries Based on Neural Network

Published: 20 October 2020 Publication History

Abstract

Sea level rise is a slow-onset natural disaster, with cumulative and gradual nature. It has a considerable impact on human survival, economic development and cultural inheritance [1]. Coupled with the impact of land subsidence in many areas along the coast, sea level rise may reach 1 meter or even higher within a century [2]. In order to meet the increasingly severe energy policy challenges and protect the unique culture that is about to disappear, it is important to establish a model that can assess whether a country or region has migration risks.
This article will build a neural network model to assess the risk of each island country and predict its possibility of becoming a climate refugee country, in order to help people around the world to take precautionary measures in advance, and help risky countries prepare for migrating nationals in advance. And through further analysis of the data of 11 countries that have been determined by the Office of the United Nations High Commissioner for Refugees in Syria, the model construction and model calculation are carried out [3]. Finally, according to the imbalanced population distribution index and population density concentration index of 11 countries [4], we can know that in Bosnia and Herzegovina the risk of losing the culture of Syrian refugees is relatively low.

References

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Gao J, Zhang Y C and Zhou T (2019). Computational Socioeconomics[J]. Physics Reports.
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Vo Thanh and Sugai Sasaki (2020). Impact of a new geological modelling method on the enhancement of the CO2 storage assessment of E sequence of Nam Vang field, offshore Vietnam[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(12).
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Farbotko C and Lazrus H (2012). The first climate refugees? Contesting global narratives of climate change in Tuvalu[J]. Global Environmental Change, 22(2), 382--390.
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Gu Junjie (2016). Analysis on population spatial pattern and influencing factors of Inner Mongolia Based on Grid GIS[D].
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Chen Junning (2014). Research on island countries climate refugees[D]. Soochow University.
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Koo Jiyoung (2019). International environmental law on sea-level rise due to climate change - a case study of the centre for the protection of small island states[J]. Legality Vision, 34, 103--104.
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Liu Jing and Liu Hong (2007). RBF networks ensemble based on island migrating model and research of its application. Computer Engineering and Applications, 43(31), 196--198.
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Zhang Qiaoli (2017). Research on the protection of "environmental refugees" in the context of international law[D]. Xiamen University.
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Cao Zhijie and Chen Shaojun (2012). Analysis of Climate Migrants' Migration Mechanism Status and fissure Perspective of Climate Risk[J]. Chinese Population, Resources and Environment, 22(011), 45--50.

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CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering
October 2020
1038 pages
ISBN:9781450377720
DOI:10.1145/3424978
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2020

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Author Tags

  1. Cultural loss
  2. Environmentally displaced persons
  3. Migration risk
  4. Neural networks

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  • Research-article
  • Research
  • Refereed limited

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CSAE 2020

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CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;
Overall Acceptance Rate 368 of 770 submissions, 48%

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