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基于IWOA-ALSTM的水文时间序列预测
Xuejie Zhang1,2, Hao Cang3,4, Nadia Nedjah5
1Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 211100, China. xuejie_zh@hhu.edu.cn.
Scientific reports
|April 5, 2024
概括
这项研究使用改进的鱼优化算法 (IWOA) 来增强水文时间序列预测,以优化基于注意力的长期短期记忆 (ALSTM) 网络. IWOA-ALSTM模型显著提高了预测非线性水文数据的准确性.
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
- 时间序列分析时间序列分析
背景情况:
- 准确的水文时间序列预测对于洪水/干旱管理和智能水资源至关重要.
- 水文数据中的非线性特征极大地挑战了预测的准确性.
- 优化预测模型对于有效的水资源管理至关重要.
研究的目的:
- 为了提高水文时间序列中非线性元件的预测准确度.
- 开发和评估一个改进的以注意力为基础的长期短期记忆 (IWOA-ALSTM) 网络的鱼优化算法.
- 调查优化超参数对预测性能的影响.
主要方法:
- 在LSTM层之间集成了一个注意力机制,以关注相关的时间序列特征.
- 使用改进的鱼优化算法 (IWOA) 来优化ALSTM的超参数.
- 来自汉口站的非线性水位数据被用于实验验证.
主要成果:
- IWOA有效地优化了ALSTM网络,从而提高了预测准确度.
- 拟议的IWOA-ALSTM模型与GA,PSO和WOA相比表现出优异的性能.
- 使用RMSE,MAE,NSE,SI和DR指标的评估证实了该模型的有效性.
结论:
- IWOA-ALSTM模型在预测非线性水文时间序列方面取得了重大进展.
- 使用IWOA优化超参数对于提高预测准确性和效率至关重要.
- 这种方法有助于更强大的洪水和干旱预防战略.
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