|
一种机器学习的概率分类方法
A Machine Learning Approach for Probabilistic Drought Classification |
|
| |
| 课程网址: |
http://videolectures.net/cidu2011_mallya_classification/
|
| 主讲教师: |
Ganeshchandra Mallya
|
| 开课单位: |
普渡大学
|
| 开课时间: |
2012-06-27
|
| 课程语种: |
英语
|
| 中文简介: |
目前的干旱评估方法利用干旱指数, 如标准化降水指数和帕尔默干旱严重程度指数, 这些指标依赖于主观阈值, 因此不能在不同的气候区域普遍应用。此外, 大多数现有的干旱指数不适合概率处理, 这对于量化干旱分类中的模型不确定性至关重要。本研究将一种机器学习工具--隐藏马尔可夫模型 (hmm) 应用于概率干旱分类。本研究开发的基于 hmm 的干旱指数 (hmm-di) 不需要主观阈值的具体规定, 模型参数是根据参数估计过程中的历史数据确定的。将 hmm-di 得到的干旱分类与 spi 结果进行了比较。hmm-di 揭示了对干旱的频率和严重程度及其时空变化的新见解。hmm-di 的有效性是通过其在印度各地的月降水量数据的应用来评估的。结果表明, hmm-di 可以作为传统干旱指数的一个有前途的替代品。
|
| 课程简介: |
Current methods of drought assessment utilize drought indices, such as the standardized precipitation index and Palmer drought severity index, that rely on subjective thresholds and hence cannot be universally applied across different climatic regions. In addition, most of the existing drought indices are not amenable to probabilistic treatment which is essential for quantifying model uncertainties in drought classification. This study applies a machine learning tool, the hidden Markov model (HMM), for probabilistic drought classification. The HMM-based drought index (HMM-DI) developed in this study, does not require specification of subjective thresholds and model parameters are determined from historical data during parameter estimation. The drought classifications obtained using HMM-DI are compared with SPI results. The HMM-DI reveals new insights into the frequency and severity of droughts and their spatio-temporal variations. The effectiveness of HMM-DI is assessed by its application to monthly precipitation data over India. The results suggest that HMM-DI can be a promising alternative to conventional drought indices.
|
| 关 键 词: |
计算机科学; 机器学习; 概率分类
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-06-03：张荧(课程编辑志愿者)
|
| 阅读次数: |
44
|
|
图 书 馆