研究基于合机器学习方法的最佳选择流失预测模型.
Xing Wei1, Mengen Chen2, Yulin Zhou2
1School of Civil Engineering, Chongqing Three Gorges University, Chongqing, 404100, China. weixing@sanxiau.edu.cn.
Scientific reports
|December 31, 2024
概括
使用混合机器学习方法优化流失预测模型可以显著提高准确性. 使用搜索算法和长短期记忆 (VMD-SSA-LSTM) 模型的变化模式分解在预测流量方面表现出卓越的表现.
科学领域:
- 水文和水资源管理水文和水资源管理
- 气候变化对水系统的影响
- 在环境建模中的人工智能.
背景情况:
- 由于气候变化和人类活动而导致的流水波动需要准确的预测模型.
- 传统的模型经常与水文系统的复杂,非线性动力学作斗争.
- 三峡水库区域为水资源管理提供了一个关键的案例研究.
研究的目的:
- 通过集成先进的时间序列分解和机器学习技术来优化流失预测模型.
- 评估各种混合模型的性能,以提高水文预测.
- 为了确定分解方法和优化算法的最有效的组合,用于流失预测.
主要方法:
- 基础模型的比较分析:人工神经网络 (ANN),支持矢量机器 (SVM) 和长短期记忆 (LSTM).
- 时间序列分解方法的评估:基于时间变异过器的实证模式分解 (TVF-EMD),用适应噪声 (CEEMDAN) 进行完整集体实证模式分解和变化模式分解 (VMD).
- 开发和评估混合模型,将分解方法与优化算法结合在一起:鱼优化算法 (WOA),虫优化算法 (GOA) 和子搜索算法 (SSA),与LSTM集成.
主要成果:
- 在LSTM模型中,准确度高于BP和SVM.
- VMD-LSTM模型的表现优于CEEMDAN-LSTM和TVF-EMD-LSTM,Nash-Sutcliffe效率 (NSE) 和皮尔森相关系数 (R) 相比单个LSTM提高了15.06%和6.82%.
- 与VMD-LSTM相比,VMD-SSA-LSTM模型获得了最高的准确性,进一步提高了NSE和R的13.09%和4.26%.
结论:
- 一种分解-重建策略可以提高机器学习模型的性能,用于流失预测.
- 混合模型,特别是VMD-SSA-LSTM,在水文预测准确性方面提供了显著的改进.
- 这项研究为在复杂的流域系统中开发高精度下水预测模型提供了强大的框架.
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