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基于CNN-GRU注意力模型的地下水深度预测.
Huaibin Wei1, Shumin Qiao2, Jing Liu3
1School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Environmental monitoring and assessment
|January 23, 2026
概括
一个新的混合深度学习模型 (CNN-GRU-Attention) 准确预测干旱地区的地下水深度. 这种先进的模型有助于可持续的水资源管理,甚至通过减少数据来预测水位.
科学领域:
- 水文和水资源水文与水资源
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 地下水是干旱地区的重要淡水来源,面临降雨量低和干旱的威胁.
- 有效地预测地下水位对于可持续的水资源管理至关重要.
研究的目的:
- 开发和评估混合深度学习模型,用于准确地预测地下水深度.
- 在数据稀缺和干旱情景下评估模型的性能.
主要方法:
- 进行了地下水深度动态的时空空间分析.
- 一个混合CNN-GRU-注意力模型被开发出来,集成CNN,GRU和注意力机制.
- 输入变量 (蒸发,降水,温度,提取) 使用香农法进行选择.
主要成果:
- 在CNN-GRU-Attention模型显著优于其他方法,达到高精度 (MAE:0.4-0.5,RMSE:0.5-0.6,R2:0.8-0.9).
- 该模型保持了优越的预测性能,即使数据减少了10-25%.
- 减少4200万立方米的开采被认为是干旱期间地下水稳定性的最佳选择.
结论:
- 在干旱环境中,CNN-GRU-Attention模型为地下水深度预测提供了一个强大的框架.
- 这些发现为实施有效的地下水管理策略提供了宝贵的见解,特别是在干旱期间.
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