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评估LSTM与概念模型对每小时降雨排水模拟的评估,培训时间长度不同
Mohamed M Fathi1,2, Md Abdullah Al Mehedi3, Virginia Smith3
1Department of Civil Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt. m.fathi.said0@gmail.com.
Scientific reports
|May 6, 2025
概括
深度学习模型显著改善了用于减轻洪水的排水预测. 新的长期短期记忆 (LSTM) 方法优于传统方法,特别是使用更长的训练数据,提供更有效的水文建模.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学中的人工智能
- 计算流体动力学的流体动力学.
背景情况:
- 准确的高分辨率下水预测对于洪水减缓和水资源规划至关重要.
- 水文学的传统概念模型提供了简单性,但预测准确性有限.
- 深度学习模型看起来很有前途,但往往需要大量的数据和复杂的设置.
研究的目的:
- 评估两种新的长期短期记忆 (LSTM) 模型方法的稳定性,用于高分辨率的流失预测.
- 将LSTM模型的性能与传统概念模型进行比较.
- 调查训练数据集长度对模型准确性的影响.
主要方法:
- 从基于物理的模型中利用一个世纪的气象数据集来消除测量错误.
- 开发了两个LSTM模型:一个是整合概念模型输出,另一个是使用多时间输入.
- 经过培训和验证的模型使用从7年到50年的分期.
主要成果:
- 两种LSTM模型都显示,随着训练数据长度的增加,性能得到了持续的改善.
- 超过15年的培训数据,概念模型没有显著改善.
- LSTM模型在捕获参考流量持续时间曲线方面表现出卓越的准确性.
结论:
- 新的LSTM方法为流量预测提供了一个计算效率高,准确的替代方案.
- 培训数据长度的增加有利于深度学习模型,而不是概念模型.
- 这些发现为改进水文建模和水资源管理铺平了道路.
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