...
首页> 外文期刊>Journal of Hydrology >Modeling of the daily rainfall-runoff relationship with artificial neural network
【24h】

Modeling of the daily rainfall-runoff relationship with artificial neural network

机译:用人工神经网络模拟日降雨-径流关系

获取原文
获取原文并翻译 | 示例
           

摘要

An approach for modeling daily flows during flood events using Artificial Neural Network (ANN) is presented. The rainfall-runoff process is modeled by coupling a simple linear (black box) model with the ANN. The study uses data from two large size catchments in India and five other catchments used earlier by the World Meteorological Organization (WMO) for inter-comparison of the operational hydrological models. The study demonstrates that the approach adopted herein for modeling produces reasonably satisfactory results for data of catchments from different geographical locations, which thus proves its versatility. Most importantly, the substitution of the previous days runoff (being used as one of the input to the ANN by most of the previous researchers), by a term that represents the runoff estimated from a linear model and coupling the simple linear model with the ANN may prove to be very much useful in modeling the rainfall-runoff relationship in the non-updating mode. (C) 2003 Elsevier B.V. All rights re served. [References: 34]
机译:提出了一种使用人工神经网络(ANN)对洪水事件期间的每日流量进行建模的方法。通过将简单的线性(黑匣子)模型与ANN耦合,可以模拟降雨径流过程。该研究使用了来自印度两个大型集水区的数据以及世界气象组织(WMO)较早使用的其他五个集水区的数据,用于相互比较业务水文模型。研究表明,本文采用的建模方法对于来自不同地理位置的流域数据产生了合理令人满意的结果,从而证明了其多功能性。最重要的是,用代表线性模型估算的径流量并将简单线性模型与ANN耦合的术语代替前几天径流(被大多数先前的研究人员用作ANN的输入之一)。对于非更新模式下的降雨-径流关系建模,可能会非常有用。 (C)2003 Elsevier B.V.保留所有权利。 [参考:34]

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