评估大/堤运营对韩国各地使用LSTM进行流量预测的影响
Yongsung Kwon1, YoonKyung Cha1, Yeonjeong Park2
1Department of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, 02504, South Korea.
Scientific reports
|June 8, 2023
概括
包括水和的运营数据显著改善了长期短期记忆 (LSTM) 流量预测. 了解水运行特征是准确水文建模的关键.
科学领域:
- 水文学的水文学
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 深度学习,特别是长期短期记忆 (LSTM),越来越多地用于流量预测.
- 传统的LSTM模型仅依赖于天气数据,在有人工水资源管理结构 (如水和水) 的地区可能不那么准确.
- 评估纳入水运营数据的影响对于提高水文预测准确度至关重要.
研究的目的:
- 评估LSTM对韩国河流的预测准确度.
- 调查大/运营数据对LSTM模型性能的影响.
- 在结合运营数据时,比较不同的LSTM模型配置 (单个与单个).
主要方法:
- 设计了四个场景,使用25个流水站的天气数据和水/运行数据.
- 场景涉及使用单个LSTM模型或每个站的单个LSTM模型.
- 使用纳什-萨克利夫效率 (NSE) 和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 纳入/运营数据改善了LSTM的性能,增加了NSE的平均值0.182-0.206,并减少了RMSE的平均值78.2-79.6.6.
- 第4场景 (个别的LSTM模型与操作数据) 产生了最佳表现 (平均NSE=0.592,平均RMSE=181.1).
- 性能改进因大/运营特征而异,更高的频率和排放导致更好的预测.
结论:
- 包括水/运行数据显然提高了LSTM流量预测的准确性.
- 当操作数据可用时,个性化的LSTM模型通常会超过单个模型.
- 了解水和堤的特定操作特性对于优化流量预测模型至关重要.
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