通过将整体深度学习模型与元启发式算法和干旱地区的数据预处理相结合,预测每日蒸发
Tonglin Fu1, Dong Wang2, Jing Jin3
1School of Mathematics and Information Engineering, Longdong University, Qingyang, China. futonglin2008@163.com.
Scientific reports
|August 20, 2025
概括
这项研究引入了一种新的混合深度学习模型,用于准确的蒸发估计,这对于水资源管理至关重要. 与现有方法相比,WOA-VMD-CNN-SSA-BiLSTM模型显著提高了预测准确度.
科学领域:
- 环境科学 环境科学
- 水文学 水文学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 精确的蒸发估计对于管理稀缺的农业水资源至关重要.
- 使用时间序列分析的现有数据驱动模型往往缺乏高精度和普遍性.
- 对元启发式算法,集体深度学习和用于蒸发预测的先进数据预处理的整合尚未得到充分探索.
研究的目的:
- 通过时间序列分析开发一个高度准确和通用的数据驱动模型,用于精确的蒸发估计.
- 通过整合变化模式分解 (VMD),卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和优化算法,提出一种新的混合深度学习模型.
主要方法:
- 提出了一种新的混合模型:鱼优化算法-变化模式分解-卷积神经网络-子搜索算法-双向长期短期记忆 (WOA-VMD-CNN-SSA-BiLSTM).
- 使用WOA优化的VMD,从蒸发时间序列中提取多尺度特征,克服参数设置中的主观性.
- 使用CNN-BiLSTM作为核心估计模块,SSA优化其超参数.
主要成果:
- 优化的VMD参数 (k=6,0.1773) 导致样本率 (SEn) 低至0.0832,表明有效的幅度依赖特征提取.
- 在WOA-VMD预处理显著提高了CNN-SSA-BiLSTM模型的性能.
- 最终的混合模型 (WOA-VMD-SSA-CNN-BiLSTM) 在测试阶段表现出优异的性能,MSE=0.1258,RMSE=0.3547,MAE=0.2833和MAPE=6.17%.
结论:
- 拟议的WOA-VMD-CNN-SSA-BiLSTM模型为蒸发估计提供了一个高度准确和强大的方法.
- 这种混合深度学习模型的性能优于其他现有的混合和组合模型.
- 该模型被推用于估计蒸发的实际应用,以帮助农业水资源管理.
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