How,does,climate,change,influence,regional,instability

　　[Abstract]In allusion to how climate change influence regional instability， we collected 14 kinds of representative data about population， climate， production and social structure from 178 countries for the last 10 years. We analysis the indicators related to “the extent that climate change influence regional instability” and the interventions used for “mitigating the risks of climate change and preventing the country from becoming a fragile state” by using BP（Background Propagation）neural network， ISM（Interpretation Structure Model）， Three-Factor Analysis Method， STIRPAT Assessment and other methods.
　　[Keywords]Climate Change； Fragility； Data Mining； BP Neural Network
　　中圖分类号：R61 文献标识码：A 文章编号：1009-914X（2019）13-0287-02
　　1、Background
　　Climate problems loom large in recent decades， the climate change and its adverse effects are issues of common concern for human beings. From the beginning of the Industrial Revolution through now， climate change poses serious challenges to the survival and development of human society. [1]
　　“Climate change affects every aspect of our life”， former U.S president Barack Hussein Obama Jr. said at a conference in 2015. As one of the world’s most important issues， climate change is not only related to global economic growth prospects and national benefits， but also closely linked to the survival and development of hundreds of millions of people. [2] The destabilizing factors of climate can directly or indirectly affect the fragility of a country. Interacting with poor governance， societal inequalities， and a bad neighborhood， these factors in turn may promote political and economic instability， social fragmentation， migration， and inappropriate responses from governments. [3，4，5，6]Therefore， the climate issue should not be underestimated.
　　2、Analysis of the Task
　　We collect data and use BP neural network to solve this problem. We use AdaGrad as optimization algorithm and Sigmoid as excitation function. Specific programs are as follows：
　　First， we establish the relationship between country’s basic data and country’s fragility， we collected 14 kinds of representative data about population， climate， production and social structure from 178 countries for the last 10 years. In AHP， hierarchy analysis， entropy method， loss function and numerous of methods， we select BP neural network in machine learning. The model is obtained through sample learning. In the process of looking for samples， we collected a lot of data， choosing average annual precipitation and average annual temperature which are related to climate as the input indicators to the model. Then establish the relationship between climate and fragility by using BP neural network. The results show that the larger the output is， the more fragile the country is.

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