• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >A stochastic model based on artificial neural networks for synthetic streamflow generation applied to probabilistic management of droughts (Spanish text).
【24h】

A stochastic model based on artificial neural networks for synthetic streamflow generation applied to probabilistic management of droughts (Spanish text).

机译：一种基于人工神经网络的随机模型，用于合成流的生成，应用于干旱的概率管理（西班牙语）。

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

A new methodology for generating synthetic streamflow series was investigated; it was sought that the statistical properties—specially the drought statistics—of the historical series could be better reproduced than by using the traditional linear models. As a result, a stochastic multivariate non-linear model based on multilayer perception artificial neural networks was built, which fitted the proposed objective. Once the model formulation was developed, it was used to analyze four case studies, in which ARMA and disaggregation models were also applied. The case studies consisted of generating a number of synthetic monthly streamflow series by the neural network model; then, identical calculations were carried out using ARMA models and disaggregation models, in order to compare their statistic preservation capabilities. Afterwards, the probabilistic simulation of the management of several water resources systems was performed, considering states of drought. The results yielded by the neural network model show that it outperforms the traditional linear models, and has a longer hydrologic memory than these latter. Therefore, the neural network model is a high performance new alternative into the field of time series synthetic generation.
机译：研究了一种生成合成流序列的新方法。人们希望，与使用传统的线性模型相比，可以更好地重现历史系列的统计属性，特别是干旱统计。结果，建立了一个基于多层感知人工神经网络的随机多元非线性模型，该模型符合提出的目标。一旦建立了模型公式，就可以用来分析四个案例研究，其中还应用了ARMA和分解模型。案例研究包括通过神经网络模型生成多个合成的每月流量序列；然后，使用ARMA模型和分解模型进行了相同的计算，以比较它们的统计保存能力。然后，考虑了干旱状​​况，对几个水资源系统的管理进行了概率模拟。神经网络模型产生的结果表明，它优于传统的线性模型，并且比后者具有更长的水文记忆。因此，神经网络模型是时间序列综合生成领域中的一种高性能的新选择。

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