实时水库运行,使用水库调节的河流流域的流入量和灌需求预测
Kallem Sushanth1, Ashok Mishra1, Rajendra Singh1
1Department of Agricultural and Food Engineering, IIT Kharagpur, Kharagpur 721302, West Bengal, India.
The Science of the total environment
|September 7, 2023
概括
准确的水库流入量和灌需求预测可以改善水资源管理. 使用偏差纠正的全球预测系统 (GFS) 数据和长期短期记忆 (LSTM) 模型的混合方法可以增强实时水库运行.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 气候科学与气象学
- 环境科学中的人工智能
背景情况:
- 实时水库运行对于有效的水资源管理至关重要,但因天气依赖的流入量和灌需求的变化而受到阻碍.
- 准确预测这些参数对于优化水库系统性能和减轻洪水等风险至关重要.
研究的目的:
- 开发和评估一种混合预测方法,以利用偏差纠正的全球预测系统 (GFS) 数据,对水库流入和灌需求进行预测.
- 评估这种方法在印度达摩达河流域的实时水库运营中的有效性.
主要方法:
- 使用缩放分布映射方法对GFS预测 (1-5天领先) 的偏差纠正.
- 一种混合模型,将储存模块与长短期存储器 (LSTM) 结合起来,用于流入预测.
- 一个灌需求模块,利用偏差校正的GFS预测进行需求预测.
主要成果:
- 在预测潘切特和梅顿水库流入量 (NSE分别为0.88-0.96和0.78-0.88) 中的高准确性,最长为5天的领先时间.
- 准确的灌需求预测,在Kharif,Rabi和Boro季节内可接受范围内的百分比误差 (例如,Kharif在1天的领先时间内为9.45%).
- 通过使用开发的预测系统,Panchet和Maithon水库成功满足下游需求并减少洪水.
结论:
- 混合预测方法,利用偏差纠正的GFS数据和LSTM,显示了实时水库运营的巨大潜力.
- 该系统通过提供可靠的流入量和灌需求预测,实现高效的水资源管理,从而改善下游水分配和洪水控制.
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