Climate change analysis to study land surface temperature trends

被引:0
|
作者
Ashish [1 ]
Sabitha, A. Sai [1 ]
Bansal, Abhay [1 ]
机构
[1] Amity Univ Uttar Pradesh, Noida, India
来源
2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT) | 2017年
关键词
climate change; cluster analysis; K-means algorithm; data mining; range-series graphs;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Climate change is a matured area and it is of current social and economic concern. It has drawn political attention due to recent global efforts to curb its damaging effect. Due to increased availability of geographical data, the knowledge discovery from datasets is critical for climate change science and its impacts. This research work discusses the prospects of analysing climate change w.r.t recorded trends in abiotic factors such as land surface temperature using data mining. A region based study on India has been conducted in order to compute and analyse the recorded trends against proposition of "human influence climate change" advocated especially for developing economy. The approach used here is graphing and clustering techniques such as K-means. The experimental results show the demographics professing the ground reality of global warming.
引用
收藏
页数:8
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