用深度学习来描述干旱预测:一篇文献综述
Aldo Márquez-Grajales1, Ramiro Villegas-Vega2, Fernando Salas-Martínez3
1INFOTEC Center for Research and Innovation in Information and Communication Technologies, Circuito Tecnopolo Sur, No 112, Fracc. Tecnopolo Pocitos, Aguascalientes, 20326, Aguascalientes, México.
MethodsX
|July 11, 2024
概括
深度学习通过使用气候和植被指数 (如SPI,SPEI和NDVI) 来增强干旱预测. 长短期记忆网络 (LSTM) 是最常见的,但在美国和非洲缺乏研究.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 气候科学 气候科学
背景情况:
- 干旱预测对于减轻环境和人类影响至关重要.
- 深度学习为复杂的预测任务提供了先进的功能.
- 了解当前的方法是推动干旱预测的关键.
研究的目的:
- 审查和描述用于干旱预测的最先进的深度学习技术.
- 在干旱预测中识别常用的气候和植被指数.
- 绘制干旱预测深度学习研究的全球分布图.
主要方法:
- 对干旱预测中的深度学习应用的系统文献综述.
- 对经常使用的气候指数进行分析:标准降水指数 (SPI) 和标准降水蒸发指数 (SPEI).
- 识别流行多谱指数,特别是规范差异植被指数 (NDVI).
- 检查深度学习算法,重点关注长期短期记忆 (LSTM) 网络和混合方法.
主要成果:
- SPI和SPEI是最常用的气候指数.
- NDVI是最常用于干旱预测的多谱指数.
- 亚洲和大洋洲在研究成果方面处于领先地位,而美洲和非洲的出版物数量有限.
- 无论是独立的还是混合型的LSTM网络,都是占主导地位的深度学习方法.
结论:
- 在应用深度学习和多谱指数用于美国和非洲干旱预测方面存在重大研究缺口.
- 发展中国家为推进干旱预测研究提供了机会.
- 需要进行进一步的研究,以提高代表性不足的地区干旱预测能力.
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