贝叶斯模型通过结合深度学习模型的平均值来改善湖泊水位预测
Gang Li1, Zhangjun Liu1, Jingwen Zhang1
1Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China; Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Nanchang 330029, China.
The Science of the total environment
|October 13, 2023
概括
准确的湖泊水位 (WL) 预测对于水资源管理至关重要. 这项研究结合了深度学习模型 (LSTM,GRU,TCN) 使用贝叶斯模型平均值,以提高WL预测准确性和不确定性分析.
科学领域:
- 水文和气候科学 水文和气候科学
- 环境监测 环境监测
- 水资源管理 水资源管理
背景情况:
- 湖泊水位 (WL) 是气候变化影响的关键指标.
- 湖WL的波动会影响供水安全和生态系统的稳定.
- 准确的WL预测对于有效的水资源管理和生态环境保护至关重要.
研究的目的:
- 评估和提高用于湖水位预测的深度学习模型的准确性.
- 为了比较长期短期记忆 (LSTM),门式循环单元 (GRU) 和时间卷积网络 (TCN) 模型的性能.
- 为了提高预测准确度和量化不确定性,使用贝叶斯式模型平均 (BMA) 和蒙特卡洛采样.
主要方法:
- 应用了三个深度学习模型:LSTM,GRU和TCN.
- 使用贝叶斯模型平均 (BMA) 来合成来自单个DL模型的预测.
- 采用蒙特卡洛抽样方法计算不确定性分析的90%置信区间.
主要成果:
- 这三种深度学习模型都对WL湖的预测准确度令人满意.
- 在大多数预测场景中,GRU的表现普遍优于TCN和LSTM.
- 在80%的场景中,BMA进一步提高了预测准确性 (NSE和R2指标),始终保持高度排名.
- 不确定性分析显示,高制比率 (CR>84%) 和可靠的相对带宽 (RB) 为7天前的预测.
结论:
- 综合的BMA方法为准确的湖WL预测提供了一个强大的框架,超越了个别的深度学习模型.
- 拟议的方法简化了模型选择,同时提高了预测性能,并提供了可靠的不确定性估计.
- 该框架可适应预测其他水文变量,支持更广泛的水资源管理应用.
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