整合深度学习算法来预测蒸发和评估农作物水压力在农业水资源管理中
Mahfuzur Rahman1, Md Mehedi Hasan1, Md Anuwer Hossain1
1International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Journal of environmental management
|January 31, 2025
概括
先进的深度学习模型改善了对作物用水需求的预测,这对于气候变化中农业用水管理至关重要. 这提高了孟加拉国的灌策略和作物弹性.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化对全球农业产生重大影响,需要改进水资源管理策略.
- 准确预测作物用水需求,包括蒸发透气 (ET) 和作物用水压力指数 (CWSI),对于可持续农业至关重要.
- 现有的预测模型需要改进,以应对未来的气候场景.
研究的目的:
- 开发和评估先进的深度学习模型,用于预测ET,潜在蒸发 (PET) 和CWSI.
- 整合高分辨率的气候数据和多个共享的社会经济途径 (SSPs) 进行全面的未来气候场景分析.
- 为孟加拉国农业水资源管理提供增强的预测能力.
主要方法:
- 利用了深度学习技术:前神经网络 (FFNNs),卷积神经网络 (CNNs),封闭循环单元 (GRU) 和长短期记忆网络 (LSTM).
- 包含来自ACCCESS-ESM模型的高分辨率气候数据.
- 分析了四个共享的社会经济路径 (SSPs) 的预测,以表示未来的多样化气候条件.
主要成果:
- 通过使用选定的深度学习模型,在ET,PET和CWSI的预测准确度方面取得了显著的改善.
- 这些模型在各种未来气候场景下提供了可靠的预测.
- 确定了在预测关键水资源管理指标方面表现出色的特定深度学习架构.
结论:
- 深度学习模型为加强农业水资源管理提供了强大的框架.
- 使用先进的AI技术,可以准确预测ET,PET和CWSI.
- 这些发现为优化灌和改善孟加拉国气候变化下的作物弹性提供了可操作的见解.
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