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一个基于ICEEMDAN-NGO-LSTM合的新型流水预测模型
Chen Yang1, Yatao Jiang2, Ying Liu3
1Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou, 450046, China.
概括
精确的排水预测对于水资源管理至关重要. 一个新的ICEEMDAN-NGO-LSTM模型增强了水文预测,在预测每月河流流量趋势方面显示出高准确性.
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 计算科学 计算科学
背景情况:
- 准确的水文预测对于有效的水资源管理至关重要.
- 现有的下游预测模型在捕捉复杂,非线性趋势方面面临挑战.
- 可靠的预测对于可持续的水资源使用和规划至关重要.
研究的目的:
- 开发和评估一种新的合模型,以改进每月流量预测.
- 提高河流域水文预测的准确性和可靠性.
- 提供一种新的计算方法,用于短期流失预测.
主要方法:
- 该研究介绍了ICEEMDAN-NGO-LSTM结合模型.
- 它集成了改进的完整集体实证模式分解与自适应噪声 (ICEEMDAN) 进行数据处理.
- 该模型使用北方戈斯霍克优化 (NGO) 来进行参数优化和长短期内存 (LSTM) 来进行时间序列预测.
主要成果:
- ICEEMDAN-NGO-LSTM模型在预测月度流失趋势方面表现出卓越的表现.
- 该模型实现了5.95%的平均相对误差,远低于10%的值.
- 记录了0.9887的高纳什-萨特克利夫 (NS) 效率系数,表明与实际数据的良好一致.
结论:
- 拟议的ICEEMDAN-NGO-LSTM合模型在排水预测准确度方面取得了重大进展.
- 这种新的方法为短期水文预测提供了一种可靠的方法.
- 这些发现支持在水资源管理中有效应用先进的计算技术.
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