数据挖掘算法在高寒草地退化驱动因素相关性分析中的应用
Dixue qianyuan（2021）
Abstract
高寒草地的退化受到众多自然、人为活动的影响,退化与驱动因素之间的耦合关系复杂。本文以青海省称多县为研究区,提取2005—2014年归一化植被指数（NDVI,normalized difference vegetation index）时间序列数据集,结合温度、降水、社会经济因素,运用基于数据挖掘的提升度算法进行相关性分析,研究影响高寒草地退化指标与表示高寒草地退化指标之间两者的关系。本文采用提升度算法针对3个等级的NDVI、可食量、植株高度与相应的温度、降水、鼠害和放牧强度之间的关系进行分析,能够更精确地分析各驱动因子在不同等级取值范围下对草地不同等级退化的贡献率,发现驱动因素与草地退化之间的影响关系并不是单向的,而是到达一定的程度时会出现逆向影响关系。本研究得到如下结论:（1）草地植被覆盖度低与气温和降水存在负相关;（2）草地可食量低与气温和人口存在负相关,与牲畜存在正相关;（3）地上植株高度低与牲畜呈现正相关。
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined