使用深度学习数据融合模型方法改善降雨预测,用于观测和气候变化数据
Farhan Amir Fardush Sham1, Ahmed El-Shafie2,3, Wan Zurina Binti Wan Jaafar1,4
1Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), Kuala Lumpur, 50603, Malaysia.
Scientific reports
|July 31, 2025
概括
使用机器学习模型提高了准确的降雨预测. 有效的线性支向量机 (ELSVM) 在每天的预测中表现出色,而指数高斯过程回归 (指数GPR) 和长短期记忆 (LSTM) 显示出对长期预测的承诺.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气候科学 气候科学
背景情况:
- 准确的降雨预测对于水资源管理,防洪,农业和灾害准备至关重要.
- 传统的预测方法与降雨模式的复杂动态作斗争.
- 先进的机器学习为提高预测准确性提供了潜力.
研究的目的:
- 通过观察数据和气候预测的融合,提高降雨预测的准确性.
- 评估各种机器学习模型的性能,用于每天,3天和每周降雨预测.
- 为不同的预测间隔确定最有效的模型.
主要方法:
- 观察到的降雨数据与气候变化预测的融合.
- 评估先进的机器学习模型,包括高效线性支向量机 (ELSVM),指数高斯过程回归 (指数GPR) 和长短期记忆 (LSTM).
- 使用R2,平均绝对误差 (MAE),平均平方误差 (MSE) 和根平均平方误差 (RMSE) 等指标评估模型性能.
主要成果:
- 在每日降雨预测方面,ELSVM取得了最高的准确性 (R2 = 0.3868).
- 对于3天的预测,指数式GPR略高于LSTM (MAE=15.84,MSE=547.04,RMSE=23.39). 对于3天的预测,指数式GPR略高于LSTM (MAE=15.84,MSE=547.04,RMSE=23.39).
- 在每周预测中,LSTM显示出更高的错误率 (MAE=14.07,MSE=363.03,RMSE=19.05,R2=0.1662).
结论:
- 将机器学习与数据融合相结合,可以显著提高降雨预测的准确性和可靠性.
- 像ELSVM,指数式GPR和LSTM这样的先进模型为增强的预测系统提供了巨大的潜力.
- 这些改进的预测有助于更好地管理水资源,适应气候变化和应对灾害.
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