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The Frontiers of Society, Science and Technology, 2020, 2(16); doi: 10.25236/FSST.2020.021608.

Study on EDP Risk Based on Grey Prediction Model

Author(s)

Huiqing Hou, Zigen Ouyang

Corresponding Author:
Zigen Ouyang
Affiliation(s)

School of Mathematics and Physics, University of South China, Hunan, Hengyang, 421001, China

Abstract

Due to global warming, sea levels have risen and some island nations have been in danger of disappearing completely, which spawns a huge number of environmentally displaced persons (EDP).In order to solve the problems of resettlement of islanders with human rights and protection of indigenous culture, we have launched a study. Firstly, in order to predict the number of people at risk for EDP, a gray prediction model was used to predict the number of EDP migrants who are forced to leave their homes result environmental disasters by 2030, accounting for 42.86% of global migrants. At the same time, we established a population risk model and combined the fence map calculation function of ArcMaP software to obtain the distribution map of the population facing EDP risk. The population risk distribution map with the geographic location of the migrating country was established, high-risk areas of cultural loss was obtained, like Eastern and South Asia, Western Europe, etc. Secondly, we should raise the protection of EDP human rights and culture to the level of policy protection. After consulting the literature, we obtained seven indicators of human rights and cultural protection, establishing a multi-level comprehensive analysis model, and obtained the top four indicators of human rights and the top four indicators of cultural weights .We combined the weights of various indicators to make recommendations on the protection of human rights and cultural policies of EDP in immigrant countries.

Keywords

EDP, Grey forecasting mode, Greenhouse effect

Cite This Paper

Huiqing Hou, Zigen Ouyang. Study on EDP Risk Based on Grey Prediction Model. The Frontiers of Society, Science and Technology (2020) Vol. 2 Issue 16: 58-64. https://doi.org/10.25236/FSST.2020.021608.

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