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在巴西使用机器学习预测降雨量
Sidney T da Silva1, Letícia C Milani1, Enrique C Gabrick2
1Department of Chemical, Federal University of Paraná, Curitiba 81531-980, PR, Brazil.
Chaos (Woodbury, N.Y.)
|July 7, 2025
概括
机器学习模型准确地预测了巴西五个地区的降雨情况. 随机森林模型显示出最佳性能,超过了用于降雨预测的循环神经网络.
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:
- 机器学习 (ML) 对于预测气候模式和极端天气事件至关重要.
- 准确的降雨预测有助于农业,水资源管理,能源和公共安全.
- 机器学习模型可以预测气候变化,使主动规划和减轻灾害成为可能.
研究的目的:
- 评估三个ML模型用于巴西五个地区的降雨预测.
- 为了比较随机森林,长期短期记忆和双向长期短期记忆模型的性能.
- 用气候再分析数据评估ML在预测降雨模式中的有效性.
主要方法:
- 利用随机森林,长期短期记忆 (LSTM) 和双向长期短期记忆 (BiLSTM) 模型.
- 训练模型使用局部温度和大西洋温度作为输入特征.
- 用总降水量作为预测目标变量,在巴西五个地区进行预测.
主要成果:
- 所有评估的ML模型都在降水预测方面表现出令人满意的表现.
- 与LSTM和BiLSTM相比,随机森林模型实现了较低的平均绝对误差.
- 这项研究证实了ML技术在预测降雨模式方面的有效性.
结论:
- 机器学习模型是巴西降雨预测的有效工具.
- 随机森林为降雨预测提供了一个强大的,准确的方法.
- 准确的降雨预测可以提高气候变化适应和资源管理战略.
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