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在内陆水环境中的水质的长期短期记忆模型
JongCheol Pyo1, Yakov Pachepsky2, Soobin Kim3,4
1Department for Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
Water research X
|December 15, 2023
概括
深度学习,特别是长短期记忆 (LSTM) 网络,对预测水质有很大的前景. 像数据预处理和合网络这样的先进技术提高了LSTM模型在水文应用中的性能.
科学领域:
- 环境科学环境科学
- 水文学的水文学
- 计算机科学 计算机科学
背景情况:
- 水质受气候,土地使用和季节等动态变量的影响.
- 深度学习模型擅长识别水质数据中的复杂模式.
- 长短期记忆 (LSTM) 网络是熟练进行时间序列预测的循环神经网络.
研究的目的:
- 审查LSTM模型用于水质预测的应用.
- 评估独立和增强的LSTM模型,包括数据预处理和联网.
- 讨论静态变量的影响和基于LSTM的水质建模的未来挑战.
主要方法:
- 独立的长短期内存 (LSTM) 网络应用程序的审查.
- 数据预处理技术的整合:用自适应噪声 (CEEMDAN) 和同步挤压波形转换 (SWT) 完成集体实证模式分解.
- 结合网络的探索:LSTM与卷积神经网络 (CNN),注意力网络和转移学习.
主要成果:
- 独立的LSTM模型在预测水质方面表现出强度和准确性.
- 改进的LSTM模型,特别是合网络,与独立版本相比,显示出更高的性能.
- 结合静态变量和先进的预处理技术进一步完善预测能力.
结论:
- LSTM网络是用于水质时间序列预测的强大工具.
- 先进的LSTM架构和数据处理方法提高了预测准确性和稳定性.
- 未来的研究应该专注于优化这些模型用于各种水文应用.
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