Artificial Neural Networks for event based rainfall-runoff modeling.
Artificial Neural Networks for event based rainfall-runoff modeling.
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DOI：
10.4236/jwarp.2012.410105
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发表时间：
2012-01-01
期刊：
影响因子：
--
通讯作者：
Kumar, R.
中科院分区：
文献类型：
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作者：
Archana Sarkar, Archana Sarkar;Rakesh Kumar, Rakesh Kumar;Kumar, R.
人工神经网络（ANN）方法已成功地应用于许多水文研究，特别是利用连续数据的径流模拟。本研究探讨其适用性，以模拟基于事件的径流过程。以Ajay河流域为例，开发了基于事件的流域径流模型，模拟了Sarath测站的小时径流。结果表明，人工神经网络模型能够提供一个很好的代表性的事件为基础的径流过程。在预测洪水过程线时，两个重要的参数是洪峰流量的大小和到达洪峰流量的时间。开发的人工神经网络模型已经能够预测这一信息具有很高的精度。这表明人工神经网络可以非常有效地模拟基于事件的径流过程，以非常准确地确定峰值流量和峰值流量的时间。这在水资源设计和管理应用中很重要，其中峰值排放和峰值排放时间是重要的输入变量。
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.