干旱预测:来自LSTM和多源因素的融合的见解
Tian Wang1, Xinjun Tu2, Vijay P Singh3
1Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
The Science of the total environment
|August 18, 2023
概括
准确的干旱预测对于水资源管理至关重要. 本研究引入了长短期记忆 (LSTM) 框架,整合了多种因素,以提高干旱预测的准确性和可靠性,以适应气候变化.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 准确的干旱预测对于水资源管理和农业在全球气候变化中至关重要.
- 使用历史数据的传统预测方法在捕捉长期气候变化影响方面存在局限性.
- 现有的干旱预测评估往往缺乏对复杂影响因素的全面分析.
研究的目的:
- 提出并评估使用长短期记忆 (LSTM) 的新型干旱预测和评估框架.
- 整合多源因素,以提高干旱预测模型的准确性和可靠性.
- 为了比较两个不同的基于LSTM的干旱预测预测方案.
主要方法:
- 开发了一个LSTM框架,整合了十个不同的因素:降水,蒸发,土壤水分,温度,植被覆盖和下水.
- 实施了两个预测方案: (1) 预测降水和蒸发以计算标准化降水蒸发指数 (SPEI),以及 (2) 直接预测使用输入因子的SPEI.
- 在两个方案之间比较了预测准确性,干旱特征和空间模式.
主要成果:
- 该LSTM模型在处理高维数据和预测降水,蒸发,温度和土壤湿度等关键气候变量方面取得了显著的准确性改进.
- 方案1在预测严重和极端干旱方面表现出色.
- 图表2显示了对中度和轻度干旱的更高灵敏度,空间变化预测的稳定性和规律性得到改善.
结论:
- 基于LSTM的干旱预测框架在准确性,稳定性和可靠性方面提供了显著的改进.
- 综合多因素方法为农业和水资源管理中的实际应用提供了更强大的支持.
- 这项研究为气候变化影响评估和干旱预测提供了有价值的新研究工具.
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