| 来源类型 | Project Reports |
| 规范类型 | 报告 |
| Big data analysis : application to environmental research and service | |
| Sung Won Kang | |
| 发表日期 | 2017-12-31 |
| 出版年 | 2017 |
| 语种 | 英语 ; Korean |
| 摘要 | The key advantages of Machine Learning analysis using large data are 1) accurate forecast and 2) unknown-pattern finding In this report, we try to make use of these advantages in Environmental Research and Service. This research is composed of three components. First, we apply Machine learning algorithm to environmental research. (2017~19) Second, we accumulate data and algorithms developed in environmental research and combine them with environmental data web crawling algorithm to build environmental machine learning platform(2020~22). Third, we develop public environmental service using these research results and platform(2023~25). In 2017, we developed three machine learning algorithms applied to environment data ？ LSTM algorithm estimating hourly find dust pollution, Random Forest/Boosting ensemble algorithm estimating monthly find dust pollution, DNN algorithm estimating intestinal infection case numbers using climate data. Also we applied LDA/Association Rule Learning/word2vec algorithm to online news data and KEI report data, and found that KEI should pay more attention to generic mutation, noise, environmental health, environmental data and specific climate issues like typhoon, severe cold, heavy snow to catch up with public interests represented in online news data. |
| 来源智库 | Korea Evironment Institute (Republic of Korea) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/198793 |
| 推荐引用方式 GB/T 7714 | Sung Won Kang. Big data analysis : application to environmental research and service. 2017. |
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| ì¬ì 2017_07_ê°ì±（11961KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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