基于动态分类和注意力机制的双向长期短期记忆网络,用于在中国西北部阿克苏河流域每日排水预测
Qing Wei1, Ju Yang2, Fangbing Fu3
1College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China.
Journal of environmental management
|January 16, 2025
概括
准确的每日河流流量预测对于干旱地区的生态至关重要. 一个新的CA-BiLSTM模型动态分类流动模式,大大提高了比传统方法的预测准确度.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 内陆河流的流量变化对于区域生态稳定至关重要.
- 在干旱地区每日流量预测对于了解水体生态和促进河流健康至关重要.
- 准确的排水预测支持生态评估,管理和决策.
研究的目的:
- 提出一种综合建模方法,以提高干旱地区每日流水预测的准确性.
- 通过结合动态分类来解决传统模型在处理各种流动模式方面的局限性.
- 提高对水文数据关系和季节变化中的信息的理解.
主要方法:
- 开发了一种混合模型,集成了动态分类方法,注意力机制和双向长期短期记忆网络 (CA-BiLSTM).
- 使用相关水文变量的动态变化间隔确定分类边界.
- 将CA-BiLSTM性能与传统的LSTM和BiLSTM模型进行比较,使用来自阿克苏河流域的数据.
主要成果:
- 与传统的LSTM和BiLSTM模型相比,CA-BiLSTM模型在所有季节都表现出卓越的性能.
- CA-BiLSTM显著降低了预测误差:平均绝对误差 (MAE) 减少了42.99%,根平均平方误差 (RMSE) 减少了36.89%,平均绝对百分比误差 (MAPE) 减少了49.73%.
- 该模型增强了准确度指标,R2增加了10.47%和Kling-Gupta效率 (KGE) 增加了11.76%.
结论:
- 拟议的CA-BiLSTM模型通过动态适应多样化的流量模式,有效地提高了干旱地区每日流量预测的准确性.
- 这种混合方法减少了排水预测的不确定性,为干旱环境中有效的水资源管理提供了宝贵的见解.
- 该研究强调了人工智能驱动的动态分类方法在改善水文预测和生态稳定方面的潜力.
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