提高每日泛蒸发 (Evp) 预测的性能,使用集体实证模式分解与深度学习模型相结合
Zahra Kayhomayoon1, Naser Arya Azar2, Sami Ghordoyee Milan3
1Department of Geology, Payame Noor University, Tehran, Iran.
Scientific reports
|December 4, 2025
概括
本研究引入了一种新方法,用于使用最佳输入组合和先进的人工智能模型预测每日面蒸发 (Evp). 这种方法提高了水资源管理,特别是在干旱地区.
科学领域:
- 水文和水资源水文与水资源
- 环境科学中的人工智能
- 气候变化影响评估
背景情况:
- 泛蒸发 (Evp) 是水资源管理的关键因素,特别是在干旱和半干旱地区.
- 准确的EVP预测对于有效分配水和最大限度地减少水损失至关重要.
- 传统的EVP预测模型经常与气象数据的复杂,非线性动态作斗争.
研究的目的:
- 开发和评估一种用于预测每日泛蒸发 (Evp) 的新方法.
- 为了确定一个最佳的输入变量组合为Evp建模.
- 使用集体实证模式分解 (EEMD) 与长短期记忆 (LSTM) 和卷积神经网络 (CNN) 模型来提高Evp预测的准确性.
主要方法:
- 使用马测试和遗传算法 (GTGA) 进行了输入变量选择.
- 集体实证模式分解 (EEMD) 用于将输入数据 (温度,降水,过去的EVP) 分解为内在模式函数 (IMF).
- 用LSTM和CNN模型与分解的IMF作为EVP预测的输入.
主要成果:
- 该CNN模型实现了0.33毫米的根平均平方误差 (RMSE),0.24毫米的平均绝对误差 (MAE) 和0.06.06的技能指数 (SI).
- 该LSTM模型表现出优异的性能,RMSE为0.043毫米,MAE为0.11毫米,SI为0.016.
- 分解为IMF简化了数据模式,显著提高了CNN和LSTM模型的性能.
结论:
- 拟议的GTGA-EEMD-LSTM/CNN方法为每日泛蒸发预测提供了强大而准确的方法.
- 这种方法为水资源管理者提供了宝贵的见解,有助于制定节水战略,特别是在缺水环境中.
- 该方法可适应不同地理区域的EVP预测,有助于更好地适应气候变化.
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