建模淡水产量:在伊朗的海水温室中深度学习应用
Amirhossein Barzigar1, Sevda Allahyari1, Mohsen Fathi2
1School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.
Scientific reports
|October 21, 2025
概括
这项研究开发了一种深度学习模型,用于预测干旱地区的海水温室 (SWGH) 的淡水产量. 该CNN-LSTM模型实现了高精度,估计可持续水资源管理的平均年产量为1454.25 L/m2.
科学领域:
- 环境科学 环境科学
- 可再生能源工程可再生能源工程
- 水资源管理 水资源管理
背景情况:
- 海水温室 (SWGH) 为淡水生产和控制农业提供了可持续的解决方案,特别是在干旱的沿海地区,如马克兰地区.
- 将SWGH与绿色建筑集成,通过减少对传统水源的依赖和降低碳排放,提高了可持续性.
- 马克兰海岸面临着严重的淡水短缺,这使得SWGH技术成为应对这些挑战的有希望的方法.
研究的目的:
- 开发和评估基于深度学习的预测模型,以优化SWGH的淡水生产.
- 通过分析环境和运行参数,包括预测的太阳辐射来预测淡水产量.
- 评估将SWGH整合到可持续水资源管理的绿色建筑框架中的可行性.
主要方法:
- 采用了两阶段的深度学习方法,利用CNN-LSTM,BiLSTM,BiGRU,CNN-GRU和MLP等模型.
- 全球水平辐射率 (GHI) 首先被预测,其次是基于预测的太阳辐射的淡水产量估计.
- 模型性能使用R2,RMSE和MSE指标进行评估.
主要成果:
- 该CNN-LSTM模型表现出卓越的准确性,实现R2为0.9727,RMSE为0.0021,MSE为0.0022.
- 在2024-2033年期间,平均年淡水产量估计为1454.25 L/m2.
- 该研究成功预测了每单位面积的淡水生产率.
结论:
- 深度学习模型,特别是CNN-LSTM,对于准确预测SWGH系统中的淡水产量是非常有效的.
- 通过预测建模优化SWGH技术,为干旱沿海地区的水资源管理提供了可行和可持续的解决方案.
- 这些发现支持将SWGH整合到绿色建筑设计中,以提高水和能源的可持续性.
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