使用混合模型进行气象干旱严重程度预测
Aaditya Ahire1, Nilima Zade1, Umeed Mujawar1
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India.
MethodsX
|July 18, 2025
概括
由于气候变化,准确的干旱预测至关重要. 组合模型结合了极端梯度增强 (XGBoost),长短期记忆 (LSTM) 和TabNet,比传统方法显著提高了预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气象学 天气学
背景情况:
- 由于气候变化,干旱的频率和强度正在增加,对生态系统,农业和水资源造成重大破坏.
- 传统的干旱预报方法在气象数据中的复杂的时空相关性方面存在困难.
- 准确的干旱预测对于有效的灾害管理和资源规划至关重要.
研究的目的:
- 通过集成先进的机器学习技术,开发一个高度准确的干旱预测模型.
- 为了利用极端梯度增强 (XGBoost),长短期记忆 (LSTM) 和表格网络 (TabNet) 的优势,改进干旱严重程度的预测.
主要方法:
- 通过结合XGBoost用于特征选择,LSTM用于时间模式分析,TabNet用于基于注意力的洞察,创建了一个集合模型.
- 该模型利用了大量的气象数据集,包括温度,降水,湿度和风速.
- 使用诸如根平均平方误差 (RMSE),平均绝对误差 (MAE),确定系数 (R2),纳什-萨特克利夫效率 (NSE) 和克林-古普塔效率 (KGE) 等指标来评估性能.
主要成果:
- 拟议的整体模型与单个模型相比显示出更高的性能.
- 获得了最低的RMSE (0.6582) 和MAE (0.5377),以及最高的R2 (0.5069).
- 产生了最好的NSE (0.5107) 和KGE (0.6039),证实了其在干旱严重程度预测中的有效性.
结论:
- 整体模型在干旱预测中的传统方法表现明显优于整体模型.
- 这种先进的方法有助于提供早期干旱预警,并支持节水规划.
- 集成XGBoost,LSTM和TabNet为预测干旱严重程度提供了一个强大的解决方案.
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