• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Hydrology >Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model
【24h】

Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model

机译：利用深层学习模型再现液体分析变量的长期记忆的随机仿真

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Stochastic simulation has been employed for producing long-term records and assessing the impact of climate change on hydrological and climatological variables in the future. However, traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the preservation of long-term memory. However, the Long Short-Term Memory (LSTM) model, one type of recurrent neural network (RNN), employed in different fields, exhibits a remarkable long-term memory characteristic owing to the recursive hidden and cell states. The current study, therefore, applied the LSTM model to the stochastic simulation of hydroclimatological variables to examine how good the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models. The simulation involved a trigonometric function and the Rossler system as well as real case studies for hydrological and climatological variables. Results showed that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This better representation of the long-term variability can be critical in water manager since future water resources planning and management is highly related with this long-term variability. Thus, it is concluded that the LSTM model can be a potential alternative for the stochastic simulation of hydroclimatological variables. Also, note that another long-term memory model such as Gated Recurrent Unit can be also applicable.
机译：随机模拟已用于生产长期记录，并评估将来对气候变化对水文和气候变量的影响。然而，由于长期记忆的保存，传统的循环性变量的随机模拟通常低估了较大时间尺度的变化和相关结构。然而，由于递归隐藏和小区状态，长短期内存（LSTM）模型，在不同领域采用的一种复发性神经网络（RNN）表现出显着的长期存储器特性。因此，目前的研究将LSTM模型应用于水皮性变量的随机模拟，以检查LSTM模型是否可以保护长期存储器的良好并克服传统时间序列模型的缺点。模拟涉及三角函数和罗斯勒系统，以及水文和气候变量的真实案例研究。结果表明，LSTM模型再现较大时间尺度的变异性和相关结构以及比传统模型更好地更好地统计原始时域的关键统计。与其他测试的传统模型相比，在观察中含有长存储器和振荡结构的LSTM的隐藏和细胞状态允许更好的性能。由于未来的水资源规划和管理与这种长期变异性高，因此，由于未来的水资源规划和管理非常有效，这种更好的长期变异性可能是至关重要的。因此，得出结论，LSTM模型可以是水加工变量随机模拟的潜在替代方案。另外，注意，另一个长期存储器模型，例如门控复发单元也可以应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号