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Lizhi Tao¹, Zhichao Cui², Yufeng He²

¹Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education & School of Geography and Environmental Science, Jiangxi Normal University, Nanchang 330022, China; Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education, School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China.

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|April 14, 2024

概括

一个可解释的多尺度长短期记忆 (XM-LSTM) 模型可以提高每日流量预测的准确性. 整合波形变换和输入选择可以提高预测,水位是关键预测因素.

科学领域:

水文和水资源水文与水资源
环境科学中的人工智能
时间序列预测时间序列预测

背景情况:

准确的每日流量预测对于有效的水文系统管理和资源规划至关重要.
传统的预测模型往往缺乏解释性,并且可能与复杂的,多尺度的水文数据作斗争.

研究的目的:

提出和评估一个可解释的多尺度长短期记忆 (XM-LSTM) 模型,用于增强的每日流量预测.
通过整合数据分解和特征选择技术来提高流量预测的准确性和可解释性.

主要方法:

开发了XM-LSTM模型,将特鲁斯波束变换 (ATWT) 纳入数据分解和Boruta算法用于输入选择.
利用层级相关性传播 (LRP) 来解释预测结果和识别关键影响因素.
将XM-LSTM与基线X-LSTM模型 (LSTM + LRP) 进行比较,使用四个长江流域站的多步前期每日流量预测.

主要成果:

无论XM-LSTM还是X-LSTM都表现出良好的流量预测能力,随着预测交付时间的增加,性能下降.
XM-LSTM模型始终优于X-LSTM,这表明ATWT在改进基于LSTM的流量预测方面的好处.
LRP分析证实了ATWT在提取相关水文信息方面的有效性,确定水位是最重要的预测因素.

结论:

拟议的XM-LSTM模型在每日流量预测准确度方面取得了重大进展,并为预测驱动器提供了宝贵的见解.
整合ATWT和Boruta算法增强了LSTM模型在水文应用中的预测能力.
XM-LSTM为提高流量预测模型的准确性和可解释性提供了一个有希望的方法.

关键词:

博鲁塔的选择选择.层层的相关性传播传播.长期短期记忆长期短期记忆流量预测流量预测在Trous波波变换中.

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