南亚的气候建模:用于降雨和温度预测的统计和深度学习
Pradeep Mishra1, Soumik Ray2, Priyanka Lal3
1College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Rewa, M.P., India.
Scientific reports
|November 1, 2025
概括
我们比较了南亚降雨量和温度的四种预测模型,找到最好的模型取决于特定的变量和区域. 这项研究为准确的水文气象预测提供了指南.
科学领域:
- 水文气象学 水文气象学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 准确的水气天气预报对于南亚气候脆弱的经济体至关重要.
- 在百年级数据上评估各种预测模型对于稳健的预测至关重要.
研究的目的:
- 为了对四种预测方法 (SARIMA,TDNN,LSTM,XGBoost) 对南亚地区的月度降雨量和温度进行基准测试.
- 开发基于跨国,跨变量性能分析的混合型号选择指南.
主要方法:
- 利用七个南亚国家的百年数据集 (1901-2023年).
- 实施了一个可重复的管道,具有统一的预处理,早期停止和交叉验证评估 (RMSE,MAPE,R2,CV-RMSE).
- 经典 (SARIMA) 和深度学习 (TDNN,LSTM,XGBoost) 模型的比较.
主要成果:
- 模型性能因变量和地区而异.
- 由于非线性动态,时间延迟神经网络 (TDNN) 和XGBoost在降雨预测方面表现出色.
- 长期短期记忆 (LSTM) 网络是最优的最低温度,捕获长期记忆.
- 季节性自回归集成移动平均 (SARIMA) 模型适用于稳定的季节性系列.
结论:
- 开发了一个混合选择指南,将变量和位置映射到最有效的预测模型.
- 生成了2024-2025年预测与不确定性波段,以帮助农业,水资源管理和防灾准备的关键决策.
- 该框架的规模和诊断促进了复制和基于证据的政策制定.
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